Attention-Based Multiple Instance Learning for DLBCL Risk Stratification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current prognostic indices, such as the International Prognostic Index (IPI), fail to provide precise risk stratification and prediction of disease progression in diffuse large B-cell lymphoma (DLBCL), leading to inadequate treatment selection and increased uncertainty in clinical outcomes.
Innovation Solution
A computer-implemented method using machine learning to process digital pathology images, generating attention scores and feature vectors through neural networks to predict disease progression, allowing for more precise risk stratification and identification of high-risk patient populations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prognostic indices (IPI) are used for risk stratification, then the classification process is simple and quick, but the precision of disease progression prediction is insufficient
Solution Approach 1:
The digital pathology image is divided into multiple patches, and each patch is processed independently by the neural network to generate attention scores. This segmentation allows the system to capture localized pathological features that contribute to overall disease progression prediction, thereby improving prediction precision without requiring analysis of the entire complex image at once
Solution Approach 2:
Attention scores serve as intermediary values that bridge the gap between raw image patches and final disease progression predictions. The neural network generates attention scores for each patch, which then serve as weighted inputs to the multiple instance learning framework, enabling precise prediction while managing computational complexity through hierarchical processing
2Adaptability or versatility
If traditional prognostic indices are used, then treatment selection follows standard protocols, but targeted treatment selection for high-risk patients cannot be achieved
Solution Approach 1:
The system applies different attention weights to different patches within the same image, allowing localized regions with high pathological significance to have greater influence on the final prediction. This local quality differentiation enables the system to identify specific high-risk features within the tissue sample that would inform targeted treatment selection for individual patients
Solution Approach 2:
The patent transforms the traditional single-value prognostic index into a multi-dimensional assessment by generating attention scores across multiple patches and instances. This dimensional expansion preserves detailed patient-specific risk information that can guide personalized treatment decisions, moving beyond the limited granularity of traditional indices
3Productivity
If comprehensive clinical trials are conducted without precise risk identification, then all patient populations are studied, but the time and cost of clinical trials increases
Solution Approach 1:
The system performs preliminary risk stratification using digital pathology image analysis before patients are enrolled in clinical trials. By pre-identifying high-risk patient populations through the attention-based multiple instance learning framework, the system enables researchers to target specific patient groups for particular trial interventions, reducing the overall time and cost of clinical trial programs
Solution Approach 2:
The patent changes the parameter of patient selection from broad demographic criteria to precise pathological feature-based risk assessment. By using attention scores and instance-level predictions to identify high-risk patients, the system enables more efficient clinical trial design that focuses resources on the patient population most likely to benefit from new therapies
Data Source
AI summary
Systems and methods relate to predicting disease progression by processing digital pathology images using neural networks. A digital pathology image that depicts a specimen stained with one or more stains is accessed. The specimen may have been collected from a subject. A set of patches are defined for the digital pathology image. Each patch of the set of patches depicts a portion of the digital pathology image. For each patch of the set of patches and using an attention-score neural network, an attention score is generated. The attention-score neural network may have been trained using a loss function that penalized attention-score variability across patches in training digital pathology images labeled to indicate no or low subsequent disease progression. Using a result-prediction neural network and the attention scores, a result is generated that represents a prediction of whether or an extent to which a disease of the subject will progress.