Multimodal LLM for Pathology Image Analysis
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Solution Overview
Problem
Current computational pathology systems lack integration of natural language, which is crucial for unlocking human medical knowledge, serving as a signal for model supervision, and facilitating intuitive interaction between AI models and users.
Innovation Solution
A multimodal large language model (MLLM) is developed to provide natural language decision support for pathology, combining vision encoders to process pathology images with large language models trained on pathology-related sources, enabling the system to reason over both visual and natural language inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used for pathology image analysis, then task-specific performance improves, but integration of natural language understanding and human medical knowledge deteriorates
Solution Approach 1:
The patent combines a vision encoder that processes pathology images with a large language model that understands natural language and medical knowledge. The vision encoder generates visual features from images while the LLM processes textual queries and medical knowledge, and these two components are merged into a unified system that can simultaneously handle image analysis and natural language understanding for comprehensive pathology assessment
Solution Approach 2:
The system creates a multi-functional platform that performs multiple tasks including pathology image analysis, natural language query processing, medical knowledge retrieval, and clinical decision support. This universal system can adapt to different pathology tasks and query types without requiring separate specialized models for each function
2Device complexity
If traditional pathology analysis methods are used, then system simplicity is maintained, but clinical decision-making efficiency deteriorates
Solution Approach 1:
The system introduces an intelligent intermediary layer between pathology images and clinical decision-making that automatically processes images, retrieves relevant medical knowledge, and generates diagnostic insights. This intermediary handles the complex analysis work while presenting simplified results to clinicians, thereby improving efficiency without significantly complicating the user interface or workflow
3Measurement precision
If AI models are trained on large datasets, then analysis accuracy improves, but training data requirements and computational resources worsen
Solution Approach 1:
The system uses a composite architecture combining a vision encoder pre-trained on large-scale image data with a large language model pre-trained on extensive medical text corpora. This composite approach leverages the strengths of both components - the vision encoder provides robust image feature extraction from training while the LLM contributes medical knowledge from textual training data, achieving high accuracy without requiring one model to learn everything from scratch
Data Source
AI summary
Systems and methods are provided for providing natural language decision support for pathology. A lower-dimensionality representation of each of a set of received pathology image is generated and a first set of tokens is generated from the representations of the set of pathology images by projecting the lower-dimensionality representations of the received pathology images to a same dimension as an embedding space of a large language model for text tokens or through multimodal blocks added to the large language model such as cross-attention. The large language model is trained on an instruction dataset complied from a plurality of pathology-related sources. A second set of tokens associated with a natural language prompt is received at the large language model. A response is determined from the first set of tokens and the second set of tokens at the large language model.