Tumor identification method based on multi-modal feature fusion

By employing a tumor identification method based on multimodal feature fusion, the problem of feature distribution shift caused by heterogeneous multicenter devices is solved, achieving tumor identification with high accuracy and clinical interpretability. This method is adapted to the deployment of edge medical devices and improves the detection rate of early-stage small tumors.

CN121765644APending Publication Date: 2026-03-31SINONEEDLE INTELLIGENCE TECH CO LTD
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Patent Information

Application Number
CN202512055443.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-31
Publication Date
2026-03-31

AI Technical Summary

Technical Problem

Existing tumor identification technologies suffer from problems such as lack of multimodal feature calibration, insufficient semantic association, poor generalization, and lack of clinical interpretability, especially in the case of heterogeneous multicenter equipment, feature distribution shift and sharp drop in recognition performance.

Method used

An event-driven sensor array is used to synchronously acquire visual structure and electrical impedance data. The acquisition parameters are adjusted through a biomimetic spatiotemporal perception algorithm. The structured and characteristic feature vectors are extracted by a heterogeneous twin coding engine, and dynamic weighted fusion and semantic association are performed. Multi-scale dynamic perception cell array, global topological association modeling and cross-modal calibration are used to generate comprehensive feature vectors. Semantic association is enhanced through attention mechanism and InfoNCE contrastive loss, which is suitable for deployment of edge medical devices.

Benefits of technology

It achieves a unified dimension and distribution of multimodal features, improves the accuracy and stability of tumor identification, reduces computing power consumption, meets the interpretability and generalization requirements of clinical diagnosis, and in particular improves the detection rate of early-stage small tumors.

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Abstract

The invention provides a tumor recognition method based on multi-modal feature fusion, relates to the technical field of tumor recognition, and is used for solving the problems of missing multi-modal feature calibration, insufficient semantic association, poor generalization and insufficient clinical interpretability in the prior art. The method comprises the following steps: firstly, synchronously acquiring visual structure and electrical impedance bimodal data through an event-driven sensor array, and completing preprocessing through pulse coding, STDP rule optimization and quality verification; then, respectively extracting structured feature vectors of a tumor visual structure class and characteristic feature vectors of a numerical attribute class by a heterogeneous twin coding engine, and unifying dimensions and distribution through processes such as multi-scale dynamic perception and cross-modal calibration; then based on an attention mechanism, InfoNCE contrast loss and minority class weight gain, dynamic weighted fusion and semantic association enhancement are performed on the bimodal features, and a comprehensive feature vector is generated; and finally, through clinical logic adaptation and multi-center deviation correction, outputting a tumor benign and malignant identification result through a full-connection classifier, and synchronously generating a clinical interpretable report containing key features and weights. According to the method, the complementary advantages of bimodal information are effectively integrated, the problems of heterogeneous multi-center equipment, unbalanced samples and the like are solved, the tumor recognition accuracy and the early-stage tiny tumor detection rate are improved, the computing power consumption is reduced, edge medical equipment deployment is adapted, and the requirements for low misjudgment and traceability of clinical diagnosis are met.
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