Fusion method and system of different modal features

Abstract

The technical scheme of the invention discloses a method and a system for fusing different modal features. According to the method, cross-modal attention fine alignment is carried out, related features are extracted, irrelevant information is inhibited through dynamic gating, key modals are highlighted, finally, the two modal features are combined in pairs through tensor product fusion, and fusion representation containing rich nonlinear interaction items is directly generated. The staged processing not only ensures that important cross-modal information is not lost and distorted in the fusion process, but also creates a new discrimination basis through explicit second-order features. Experiments show that the method can more effectively learn the implicit association mode between the image and the structured data, and obtains better performance in a complex decision task. The capability improvement is derived from the fundamental difference of the method in the fusion strategy: the method jumps out of a linear weighted normal form, provides a fusion architecture capable of deeply depicting the product relationship between modals, and realizes effective modeling of nonlinear multi-modal association.

Description

Technical Field

[0001] This invention relates to a method and system for fusing image features with structured data features. Background Technology

[0002] Existing medical AI methods typically employ feature fusion strategies such as direct feature concatenation, weighted average fusion, gated multimodal units (GMUs), and multi-expert hybrid systems (MMoEs).

[0003] Existing feature concatenation fusion methods often directly concatenate features from different modalities to form a longer feature vector, which is then input into the downstream model. This linear concatenation method itself does not introduce intermodal interactions; it merely discovers modal relationships through parameter learning in the subsequent model. Therefore, its ability to characterize nonlinear relationships is limited, and the model must rely on deep networks to "discover" the product relationships of features from different modalities, resulting in low efficiency.

[0004] The weighted average fusion method assigns pre-defined or simply learned global weights to each modality feature, and then linearly adds them to obtain the fused feature (e.g.: (Form). This globally weighted average cannot be dynamically adjusted according to the specific input content, nor can it characterize the interaction between specific feature dimensions: it assumes weights It applies to the entire feature space, and therefore struggles to handle complex and varied modal relationships.

[0005] GMU is a fusion unit based on gated neural networks, typically implemented using a sigmoid gate vector. Then output ( Element-wise multiplication (For activation functions). GMU can learn how to balance the information of the two modalities as a whole, dynamically determining the fusion ratio for each sample, which alleviates modal conflict and redundancy to some extent. However, GMU fusion is essentially still a linear interpolation of the entire modality feature vector: regardless of Whether it's a scalar or a vector, its function is to proportionally filter the overall contributions of the two modalities, without explicitly generating new feature components representing specific cross-modal feature interactions. In other words, the GMU output is still within the linear subspace spanned by the original features, and the model needs subsequent layers to approximately express the product relationship.

[0006] MMoE is typically used in multi-task or multi-output scenarios, but when applied to multimodal fusion, feature extractors from different modalities can be treated as "experts." A gating network learns to assign weights to each modality based on the input, and then the outputs of each expert are weighted and synthesized. This mechanism is essentially a soft-routing weighting: information from different modalities is extracted by the expert network and then linearly superimposed according to weights. Although MMoE can flexibly adjust the overall weights of each modality for different inputs (similar to a high-level form of weighted averaging), it does not produce explicit cross-modal interaction features. Each modality still extracts and contributes independently; the fusion result is merely a weighted sum of information from different sources, lacking a direct characterization of the multiplicative relationships between modalities.

[0007] The aforementioned feature fusion methods either leave the burden of learning nonlinear correlations to subsequent deep networks (such as feature direct concatenation and weighted averaging) or can only adjust modal importance overall (such as GMU and MMoE), without generating explicit feature representations that include intermodal product terms. This makes it difficult for models to efficiently capture complex relationships such as "the effect of the co-occurrence of specific image features and specific structured features," and nonlinear correlations are often submerged in linear combinations and cannot be fully utilized. Summary of the Invention

[0008] The technical problem that this invention aims to solve is that existing feature fusion methods only capture the linear relationship between modes, lack direct modeling of nonlinear multimodal correlations, and are difficult to deal with complex nonlinear correlations.

[0009] To solve the above-mentioned technical problems, the technical solution of the present invention discloses a method for fusing different modal features, characterized by comprising the following steps: Step 1: Dynamically align the input image features using a cross-modal attention mechanism. With structural features Semantic information is used to establish fine-grained associations between image modalities and structured data modalities, thereby obtaining image features aligned with semantic information. With structural features ; Step 2: Using image features and structured features As input, a gating coefficient is calculated through a dynamic gating mechanism, and image features are adaptively adjusted and fused based on the gating coefficient. and structured features The feature information is used to generate gated image features. With gated structured features ; Step 3: Using gating image features and gated structured features As input, the outer product tensor is calculated and the dimension is reduced through the tensor product fusion mechanism to capture the high-order correlation between the two modal features, and the features are fused through high-order interaction to obtain the final fused representation for downstream prediction.

[0010] Preferably, the image features Represented as an M-dimensional vector The structured features Represented as an N-dimensional vector ,in, Indicates the image features In the Values ​​in each dimension , Representing the structured features In the Values ​​in each dimension In the cross-modal attention mechanism; Through attention weight and attention weight To measure the image features With the structured features The pairwise correlations across various dimensions, among which, Indicate image features The Middle Dimension for structured features The Middle Dimensional attention weights Representing structured features The Middle Dimension for image features The Middle Attention weights for dimensions, and and Different parameter sets and calculation functions are used respectively; The image features Based on attention weights We perform a weighted summation to obtain the structured features of each dimension. Weighted structured features in the current dimension , ; The structured features Based on attention weights Weighted summation is performed to obtain the image features of each dimension. Weighted image features in the current dimension , .

[0011] Preferably, the attention weight Calculated by the following formula:

[0012] In the formula, Rate attention , , For trainable parameters, For bias terms; The attention weight Calculated by the following formula:

[0013] In the formula, Rate attention , , For trainable parameters, This is a bias term.

[0014] Preferably, the weighted structured features Represented as: ; The weighted image features Represented as: .

[0015] Preferably, step 2 includes the following steps: Step 201: Based on image features and structured features Calculate the gating coefficients for each image channel. and the gating coefficient of the structured data channel ; Step 202: Gating coefficient and gating coefficient Converted into weighting factors for fusion , ; Step 203, Image Features According to weighting factors Scaling adjustments are made, while structural features are also adjusted. According to weighting factors Scaling and adjustment are performed to obtain the gated image features. With gated structured features .

[0016] Preferably, in step 201, the gating coefficient Calculate using the following formula:

[0017] In the formula, for The parameter vector, For bias terms, Use the Sigmoid activation function; The gating coefficient Calculate using the following formula:

[0018] In the formula, for The parameter vector, For bias.

[0019] Preferably, in step 202, the weighting factor , Represented as: , .

[0020] Preferably, in step 203, the gated image features Represented as: The gated structured features Represented as: .

[0021] Preferably, step 3 includes the following steps: Step 301: Generate tensors :

[0022] tensor Each element in Corresponding to gated image features No. Dimensional components and gated structured features No. Product of dimensional components:

[0023] Step 302, for tensors Each element in the learning process learns an attention weight. This is used to reflect the importance of the interaction item; Step 303, with We then use these coefficients to weight the corresponding tensor terms, resulting in:

[0024] In the formula, These are the tensor elements after dimensionality reduction.

[0025] Another technical solution of the present invention discloses a fusion system for different modal features, characterized in that the fusion method described above includes: A cross-modal attention module is configured to dynamically align the input image features through a cross-modal attention mechanism. With structural features Semantic information is used to establish fine-grained associations between image modalities and structured data modalities, thereby obtaining image features aligned with semantic information. With structural features ; The dynamic gating module is configured to use a dynamic gating mechanism based on the input image features. and structured features After calculating the gating coefficients, the image features are then adaptively adjusted and fused using these gating coefficients. and structured features The feature information is used to generate gated image features. With gated structured features ; The tensor product fusion module is configured to calculate the features of the input gated image using a tensor product fusion mechanism. and gated structured features The outer product tensor is used to reduce the dimensionality and capture the high-order correlation between the two modal features. The features are then fused using high-order interaction to obtain the final fused representation for downstream prediction.

[0026] Compared to existing feature direct connection stitching and fusion methods, this invention explicitly introduces the representation of intermodal correlations during the feature extraction stage through cross-modal attention and tensor outer product. This enables the direct encoding of high-order combination terms between image features and structured features, reducing the model's dependence on deep parameters.

[0027] Compared to existing weighted average fusion methods, the dynamic gating fusion mechanism of this invention calculates in real time for each input. , Equal gating coefficients, adaptively generating fusion weights and This adjusts the contribution ratio of the two modalities, which is a content-based dynamic weight allocation. More importantly, the tensor product fusion subsequently used in this invention generates product terms of feature pairs in each dimension, far exceeding the range that can be represented by simple weighted sums, thus effectively modeling highly nonlinear modal correlations.

[0028] Compared to existing GMUs, this invention, after attention and gating filtering, directly generates cross-modal feature product terms through tensor outer product, making each output feature correspond to a non-linear combination of image-structured features, structurally breaking through the limitations of linear weighting. Therefore, this invention can capture finer-grained and more complex modal correlation patterns, which GMUs cannot directly achieve.

[0029] Compared to existing MMoE models, the tensor product fusion proposed in this invention couples features from two modalities into a single tensor. Each output dimension contains the interaction between the two modalities, allowing the model to directly utilize these second-order features for discrimination. Therefore, in terms of representational capability, this invention can cover cross-modal feature coupling scenarios that MMoE cannot describe.

[0030] Compared with existing technologies, this invention achieves a fusion representation containing rich nonlinear interaction terms by finely aligning and extracting relevant features through cross-modal attention, suppressing irrelevant information and highlighting key modalities through dynamic gating, and finally combining features from two modalities in pairs through tensor product fusion. This staged processing not only ensures that important cross-modal information is not lost or distorted during fusion, but also creates new discrimination criteria through explicit second-order features. Experiments show that this invention can more effectively learn the implicit correlation patterns between images and structured data, achieving superior performance in complex decision-making tasks. This improvement stems from the fundamental difference in the fusion strategy of this invention: it breaks away from the linear weighting paradigm and provides a fusion architecture that can deeply characterize the product relationships between modalities, achieving effective modeling of nonlinear multimodal correlations.

[0031] This invention integrates information at three levels: attention for alignment, gating for dynamic weighting, and tensor product for higher-order interaction. Compared to traditional linear fusion, it significantly improves cross-modal representation capabilities, making it valuable not only for detecting diabetic retinopathy but also for the multimodal diagnosis of other ophthalmic diseases such as glaucoma and macular degeneration. For example, for glaucoma, it can fuse structured data such as fundus image features and visual field examinations; for cardiovascular risk prediction, it can fuse fundus vascular imaging features and data such as blood lipids and blood pressure. As a general-purpose module, this invention can be ported to various scenarios requiring both image and numerical data, significantly improving predictive performance. This modular design also facilitates integration into existing medical AI systems, demonstrating promising practical applications. Attached Figure Description

[0032] Figure 1 This is a flowchart of the present invention; Figure 2 This illustrates the cross-modal attention mechanism; Figure 3 This illustrates the dynamic gating mechanism; Figure 4 The tensor product fusion mechanism is illustrated. Detailed Implementation

[0033] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0034] like Figure 1 As shown, one aspect of this invention is to provide a method for fusing different modal features, specifically including the following steps: Step 1: Dynamically align the input image features using a cross-modal attention mechanism. With structural features Semantic information is used to establish fine-grained associations between image modalities and structured data modalities, thereby obtaining image features aligned with semantic information. With structural features .

[0035] Unlike simply concatenating two modal features, this invention first employs cross-modal attention during the fusion stage, enabling image features to... As a query Q, structured features As keys K and values ​​V, we calculate the attention weights of image features on each dimension of structured features; conversely, we calculate the attention weights of structured features on each dimension of structured features. As a query for image features of interest The attention weight matrix obtained in this way and The importance of each dimension between the two modalities was quantified. Using these weights, weighted features incorporating information from both modalities can be generated. and For example, when a lesion is shown in a fundus image, attention guides the model to focus on related clinical indicators; conversely, when a clinical indicator is abnormal, attention is directed to the corresponding image region. Compared to simple stitching, this mechanism can capture complex relationships between modalities more precisely. Utilizing cross-modal attention, the model can also automatically find and emphasize relevant parts between modalities, achieving selective information complementarity. Furthermore, through this bidirectional attention mechanism, the present invention can dynamically align semantic information from different modalities: for example, when a suspicious lesion appears in a specific region of a fundus image, the bidirectional attention mechanism may guide the model to focus on relevant risk factors (such as abnormal blood glucose) in the patient's structured data; correspondingly, when a clinical indicator is abnormal, attention is directed to a specific potentially affected area in the image. This cross-modal attention provides more refined association modeling than static fusion, enabling real-time capture of one-to-one correspondences or contextual dependencies between modalities.

[0036] Combination Figure 2 Let the image features be represented as an M-dimensional vector. Structured data features are represented as an N-dimensional vector. ,in, The image feature vector is represented in the th... Values ​​in each dimension , The structured feature vector is represented at the th Values ​​in each dimension , and These represent the dimensionality of the two features, respectively. The cross-modal attention mechanism calculates attention weights. and attention weight This is used to measure the pairwise correlation between these two features across various dimensions. Indicate image features The Middle Dimension for structured features The Middle Attention weights for dimensions; accordingly, Representing structured features The Middle Dimension for image features The Middle Attention weights for each dimension. It is important to emphasize that... and Different sets of parameters and computation functions are used to avoid forced symmetry, thus allowing attention to exhibit an asymmetric attention pattern between the two modalities.

[0037] In a preferred embodiment of the present invention, attention weighting is used. The calculation can employ an additive (or multiplicative) scoring model. For example, in one implementation, trainable parameters are introduced. , and bias terms Attention scores are obtained by linearly combining single-dimensional features. :

[0038] in, and These are respectively applied to image features No. Dimensional components and structured features No. Dimensional components The weighting coefficients, This is a bias term.

[0039] Then, attention scores were calculated. Apply Softmax normalization (normalization along the structured feature dimension, i.e., for each fixed feature). In all Summing above to obtain attention weights:

[0040] obtained in this way This indicates that in response to structured features No. Dimensional components When focusing on image features No. Dimensional components The importance ratio.

[0041] Similarly, attention weights The calculation follows the same principle, but uses a separate set of parameters (denoted as ). , and bias The attention mechanism is designed with both functional and non-functional forms to ensure independent flexibility during back association. Correspondingly, an attention score can be defined. :

[0042] in, , , For use in attention scoring The trainable parameters. Then, on the image feature dimension... Performing Softmax normalization, we get:

[0043] This is how it was obtained. Indicating that in response to image features No. Dimensional components When focusing on structural features No. Dimensional components The weight it occupies.

[0044] Calculated and The present invention further generates weighted structured features (i.e., structured features). ) and weighted image features (i.e., image features) This allows us to obtain an intermediate representation that incorporates cross-modal information.

[0045] Specifically, based on attention weights For each dimension of structured features We obtain the weighted structured features corresponding to the current dimension by performing weighted summation on the image features. :

[0046] In the above formula, The first feature that can be regarded as the original structured feature Dimensional components Attention-weighted representation in image modal context, i.e., image features are weighted according to their respective properties. The weighted expression of "structured features under the influence of image information".

[0047] Similarly, based on attention weights For each dimension of image features Weighted image features are obtained by weighted summation of structured features. :

[0048] in, The first character representing the features of the original image Dimensional components The weighted result is obtained in the context of structured data. After the above processing, the weighted image feature vector is obtained. and weighted structured feature vectors These two vectors each incorporate important information from the other's modality: It emphasizes the components of image features most relevant to the current structured data, while This reflects the part of the structured features that is closely related to the current image content. Through a bidirectional cross-modal attention mechanism, this invention achieves information interaction and correlation modeling between modalities at the feature level, laying the foundation for subsequent fusion steps.

[0049] Step 2: Dynamic gating mechanism utilizes image features and structured features Calculate the gating coefficient, and adaptively adjust and fuse image features based on the gating coefficient. and structured features The feature information is used to generate gated image features. With gated structured features .

[0050] Considering that the importance of different modal information may vary under different circumstances, this invention designs a dynamic gating mechanism to regulate the fusion process. This dynamic gating mechanism draws inspiration from and improves upon the GMU concept. We introduce a gating network based on the input image features... and structured features The system adaptively outputs gating coefficients (weight vectors ranging from 0 to 1). These gating coefficients adjust the proportion of two modal features in the fusion result, achieving soft modality selection: for example, when the image quality is high and contains obvious lesions, the gating mechanism increases the weight of image features and reduces the proportion of structured data; conversely, when the image is blurry or occluded, the role of structured data is increased, ensuring that the model decision is not affected by single-modal distortion. The dynamic gating mechanism makes the fusion adaptive, adjusting the information flow for each individual case, thus outperforming fixed weighting or simple stitching. Through the dynamic gating mechanism, the fusion process of this invention is no longer "one-size-fits-all," but dynamically weighs the importance of image and data for each instance, enhancing robustness.

[0051] Combination Figure 3 The dynamic gating mechanism consists of two parts: the calculation of the gating coefficient and the feature readjustment based on the gating coefficient.

[0052] 1) Calculation of gating coefficient Based on image features and structured features The corresponding gating coefficients are calculated. In a preferred embodiment of the invention, a fully connected neural network or a linear transformation combined with an activation function is used to process image features. and structured features The features are compressed and mapped, and a scalar gating value is output. For example, one feasible implementation is to perform compression mapping on image features and output a scalar gating value. By applying an affine transformation and mapping it to the [0,1] range using the sigmoid activation function, the gating coefficients of the image channels are obtained. :

[0053] in: For one The parameter vector (which represents image features) Mapped to scalar); For the corresponding bias term; Use the Sigmoid activation function; The value of is between 0 and 1, representing the degree of contribution or confidence of the image modality relative to the current task.

[0054] Similarly, for structured features Perform the same processing to obtain the gating coefficients of the structured data channel. :

[0055] in, for The parameter vector, For bias, Similarly, the value ranges from 0 to 1.

[0056] parameter , , , All are learnable parameters of the model. Gating coefficients. and The importance of current image features and structural features is described respectively: The larger the value, the more the model believes that image features should play a greater role in the current situation; The larger the value, the greater the proportion of structured features should be.

[0057] (ii) Feature readjustment based on gating coefficient Two gating coefficients and This is converted into weighting factors for fusion to adjust the outputs of the image and structured feature vectors. To ensure a balance between the contributions of the two modalities, we can... and Normalization is performed to generate image fusion weights. and structured fusion weights For example, the following formula can be used: ,

[0058] when In cases like the initial state, default weights can be set as needed, or a smoothing term can be used to avoid a zero denominator. The above... and The sum is 1, thus providing a pair of complementary fusion coefficients to represent the relative fusion ratio. If Larger and Smaller, then Approximately 1 ( A value close to 0 indicates that image features play a dominant role in the fusion process; conversely, a value close to 0 indicates that image features play a dominant role in the fusion process.

[0059] Based on the calculated fusion weights, the two types of feature vectors are scaled and adjusted to obtain the gated adjusted output feature vector. Specifically, image features Multiply Structural features Multiply ,get:

[0060] in, This represents the image feature vector after gating adjustment. This represents the structured feature vector after gating adjustment. Through the dynamic gating fusion mechanism, the feature strength of each modality is adaptively adjusted according to the current multimodal context: if the information of a certain modality is more critical in the current situation, the gating mechanism will increase its overall weight, and vice versa. It is worth noting that, unlike simple linear weighting, the gating coefficients are dynamically calculated from the input features through a non-linear activation function. This allows the fusion weights to change with the sample content, thus achieving content-based adaptive fusion. This mechanism effectively filters out unimportant modal information, highlights the contribution of key modalities, and provides a cleaner and more discriminative feature representation for subsequent deep fusion.

[0061] Step 3: Using gating image features and gated structured features As input, the outer product tensor is calculated and the dimension is reduced through the tensor product fusion mechanism to capture the high-order correlation between the two modal features, and the features are fused through high-order interaction to obtain the final fused representation for downstream prediction.

[0062] Through tensor product fusion, the model can capture high-order interactions between image features and data features, such as the co-occurrence of certain feature patterns. This multi-level fusion far surpasses the expressive power of simple splicing. Tensor fusion differs from vector splicing (equivalent to first-order combination) or element-wise weighting (linear combination) in that it captures high-order correlations across modal features by calculating the cross-modal outer product tensor. In this invention, we calculate the outer product between the gated image feature vector and the structured data feature vector to obtain a three-dimensional tensor, which is then mapped back to the fused feature vector through tensor dimensionality reduction (e.g., fully connected layers or low-rank decomposition). This process is equivalent to explicitly modeling the interactions between all modal feature dimensions, representing complex combinations such as "an image feature A and a clinical indicator B are both abnormal," which simple splicing cannot directly express. To control model complexity, we can use low-rank tensor decomposition or attention-based tensor selection in the implementation to optimize the efficiency of tensor fusion.

[0063] Combination Figure 4 The outer product operation is used to gate image features. and gated structured features This is mapped to a three-dimensional tensor representation. Formally, let... , Its tensor product is defined as follows:

[0064] Received For size A three-dimensional tensor (which can be understood as containing) OK, A tensor in matrix form with column elements. Where, tensor Each element in Corresponding to gated image features No. Dimensional components and gated structured features No. Product of dimensional components:

[0065] Tensors constructed in this way It explicitly includes interactive information combining imagery and structured features in pairs: Characterizes image features in dimensionality The values ​​and structured features on the dimension The model preserves the second-order interactions between all dimensional pairs through this outer product operation, forming a rich cross-modal feature space. It's important to note that this tensor fusion method differs from simple vector concatenation or weighted averaging; it couples features from different modalities in a product form, thereby capturing feature co-occurrence relationships and non-linear correlation patterns that linear fusion cannot represent.

[0066] Due to directly generated tensors The dimensions may be high (size is) (When the modal dimension is large, a large number of feature terms will be generated). This invention employs a tensor dimensionality reduction strategy to extract effective information and control model complexity. In an embodiment of this invention, a feasible dimensionality reduction method is to flatten the tensor into a vector and then map it to the desired low-dimensional space through a fully connected layer. For example, the tensor can be... Flattened into a length of element-wise order a one-dimensional vector Then introduce a weight matrix (size is) , (Feature length after dimensionality reduction) and bias Through linear transformation To achieve dimensionality reduction, among which, This is the output feature vector after fusion. This process is equivalent to... Each interaction item is assigned an appropriate weight and a weighted sum is performed to extract the most discriminative fusion feature.

[0067] In another feasible implementation of this invention, a low-rank tensor representation is used to represent the tensor. Approximations can be made to reduce dimensionality and computational cost. For example, matrix factorization or tensor factorization techniques can be used to decompose a high-order tensor representation into a product of several low-order factors: ,in, Represents the outer product of vectors. , , The number of decomposition components for the low-rank approximation. By selecting appropriate... It can be used with far less The original tensor is approximated by its free parameters, thereby significantly reducing feature dimensionality and model complexity while preserving the main interaction information.

[0068] Furthermore, this invention can also introduce an innovative design that uses an attention mechanism to select the tensor dimension: that is, a tensor. Each element in the learning process learns an attention weight. This is used to reflect the importance of the interaction item. Specifically, an attention map can be added to the model for each interaction item. For output (For example, using an additive scoring method similar to the one described above, but with independently set parameters), and normalizing over all interactive items using Softmax. Then... As coefficients, the corresponding tensor terms are weighted:

[0069] Only when When the interaction value is large, the interaction information of that term is fully preserved during subsequent dimensionality reduction; otherwise, it is weakened or even ignored. Through this selective emphasis and suppression, the model can focus on the most informative cross-modal feature combinations and effectively filter out redundant or noisy interaction terms.

[0070] The tensor product fusion mechanism of this invention comprehensively characterizes the second-order relationship between modes through the outer product operation, and, in conjunction with strategies such as dimensionality reduction and attention filtering, suppresses dimensionality explosion and invalid feature interference while ensuring expressive power, thereby obtaining a final fused feature representation rich in nonlinear correlation information.

[0071] The above mechanisms can be integrated into an end-to-end fusion module pipeline: based on image features With structural features As input, a cross-modal attention mechanism is first executed to obtain weighted image features. and structured features Then, a dynamic gating mechanism is used to generate fusion weights to obtain gated image features. and gated structured features Finally, a tensor product fusion mechanism is applied to obtain the final joint representation. This end-to-end fusion module pipeline can be inserted into the decision layer of any multimodal model.

[0072] A second aspect of this invention discloses a fusion system for different modal features, used to implement the above-described fusion method, specifically including: A cross-modal attention module is configured to dynamically align the input image features through a cross-modal attention mechanism. With structural features Semantic information is used to establish fine-grained associations between image modalities and structured data modalities, thereby obtaining image features aligned with semantic information. With structural features ; The dynamic gating module is configured to use a dynamic gating mechanism based on the input image features. and structured features After calculating the gating coefficients, the image features are then adaptively adjusted and fused using these gating coefficients. and structured features The feature information is used to generate gated image features. With gated structured features ; The tensor product fusion module is configured to calculate the features of the input gated image using a tensor product fusion mechanism. and gated structured features The outer product tensor is used to reduce the dimensionality and capture the high-order correlation between the two modal features. The features are then fused using high-order interaction to obtain the final fused representation for downstream prediction.

[0073] To evaluate the effectiveness of this invention, we designed a reasonable experimental scenario. Taking real-world ophthalmological diagnosis as an example, we used a multimodal dataset containing fundus images and corresponding clinical data (e.g., screening data for diabetic retinopathy from a hospital, where each sample includes a fundus image and indicators such as blood glucose and blood pressure). We compared the performance of the following methods: ① traditional fusion (image features + direct data stitching); ② image-only model; ③ structured data-only model; ④ the fusion model of this invention. Evaluation metrics included AUC, F1 score, sensitivity, and specificity. In the hypothetical results, this invention achieved improvements in all metrics. For example, in a disease binary classification task, the AUC of traditional stitching fusion might be 0.88, while this invention, through refined fusion, can improve the AUC to approximately 0.92; the F1 score increased from 0.80 to 0.85, and sensitivity and specificity improved by 5-10 percentage points respectively. More importantly, through the gating mechanism, we found that in subsets with data quality deviations (such as missing clinical data for some patients or poor image clarity), the performance degradation of this invention was significantly smaller than that of the baseline model, demonstrating stronger robustness. This verifies that dynamic gating effectively mitigates the impact of unreliable single-modal information in uncertain scenarios. Simultaneously, the benefits of the attention mechanism are also evident through interpretability analysis: the attention weights show that the model does indeed focus on relevant lesion areas in the image and key indicators in the clinical data when making decisions, which aligns with the experience of medical experts. For example, for a fundus image predicted as positive, the model's attention focuses on the hemorrhage area in the image and the patient's history of high diabetes. This matching improves the credibility of the results and demonstrates that the fusion module captures the correct correlations between modalities.

Claims

1. A method for fusing different modal features, characterized in that, Includes the following steps: Step 1: Dynamically align the input image features using a cross-modal attention mechanism. With structural features Semantic information is used to establish fine-grained associations between image modalities and structured data modalities, thereby obtaining image features aligned with semantic information. With structural features Step 2: Using image features and structured features As input, a gating coefficient is calculated through a dynamic gating mechanism, and image features are adaptively adjusted and fused based on the gating coefficient. and structured features The feature information is used to generate gating image features. With gated structured features Step 3: Using gating image features and gated structured features As input, the outer product tensor is calculated and the dimension is reduced through the tensor product fusion mechanism to capture the high-order correlation between the two modal features, and the features are fused through high-order interaction to obtain the final fused representation for downstream prediction.

2. The method for fusing different modal features as described in claim 1, characterized in that, The image features Represented as an M-dimensional vector The structured features Represented as an N-dimensional vector ,in, Indicates the image features In the Values ​​in each dimension , Representing the structured features In the Values ​​in each dimension In the cross-modal attention mechanism, attention weights are used to... and attention weight To measure the image features With the structured features The pairwise correlations across various dimensions, among which, Indicate image features The Middle Dimension for structured features The Middle Dimensional attention weights Representing structured features The Middle Dimension for image features The Middle Attention weights for dimensions, and and Different parameter sets and calculation functions are used respectively; the image features Based on attention weights We perform a weighted summation to obtain the structured features of each dimension. Weighted structured features in the current dimension , The structured features Based on attention weights Weighted summation is performed to obtain the image features of each dimension. Weighted image features in the current dimension , .

3. The method for fusing different modal features as described in claim 2, characterized in that, The attention weight Calculated by the following formula: In the formula, Rate attention , , For trainable parameters, The bias term; the attention weight Calculated by the following formula: In the formula, Rate attention , , For trainable parameters, This is a bias term.

4. The method for fusing different modal features as described in claim 2, characterized in that, The weighted structured features Represented as: The weighted image features Represented as: .

5. The method for fusing different modal features as described in claim 1, characterized in that, Step 2 includes the following steps: Step 201, based on image features and structured features Calculate the gating coefficients for each image channel. and the gating coefficient of the structured data channel Step 202: Gating coefficient and gating coefficient Converted into weighting factors for fusion , Step 203, Image Features According to weighting factors Scaling adjustments are made, while structural features are also adjusted. According to weighting factors Scaling and adjustment are performed to obtain gating image features. With gated structured features .

6. The method for fusing different modal features as described in claim 5, characterized in that, In step 201, the gating coefficient Calculate using the following formula: In the formula, for The parameter vector, For bias terms, The sigmoid activation function; the gating coefficient Calculate using the following formula: In the formula, for The parameter vector, For bias.

7. The method for fusing different modal features as described in claim 5, characterized in that, In step 202, the weighting factor , Represented as: , .

8. The method for fusing different modal features as described in claim 5, characterized in that, In step 203, the gated image features Represented as: The gated structured features Represented as: .

9. The method for fusing different modal features as described in claim 1, characterized in that, Step 3 includes the following steps: Step 301, generating tensors : tensor Each element in Corresponding to gated image features No. Dimensional components and gated structured features No. Product of dimensional components: Step 302, for tensors Each element in the learning process learns an attention weight. This is used to reflect the importance of the interaction item; Step 303, with We then use these coefficients to weight the corresponding tensor terms, resulting in: In the formula, These are the tensor elements after dimensionality reduction.

10. A fusion system of different modal features, characterized in that, The method for implementing the fusion method of claim 1 includes: a cross-modal attention module configured to dynamically align input image features via a cross-modal attention mechanism. With structural features Semantic information is used to establish fine-grained associations between image modalities and structured data modalities, thereby obtaining image features aligned with semantic information. With structural features The dynamic gating module is configured to use a dynamic gating mechanism based on the input image features. and structured features After calculating the gating coefficients, the image features are then adaptively adjusted and fused using these gating coefficients. and structured features The feature information is used to generate gating image features. With gated structured features The tensor product fusion module is configured to calculate the features of the input gated image using a tensor product fusion mechanism. and gated structured features The outer product tensor is used to reduce the dimensionality and capture the high-order correlation between the two modal features. The features are then fused using high-order interaction to obtain the final fused representation for downstream prediction.

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