Generative ML Architecture for Biological Image Analysis
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Solution Overview
Problem
Current systems for analyzing biological imaging data, such as whole slide images from histology slides, lack the ability to efficiently couple imaging data analysis with generative machine learning models to provide comprehensive outputs related to biological conditions.
Innovation Solution
The proposed architecture integrates a computing system that analyzes biological imaging data by coupling it with generative machine learning models. This integration is achieved through various training processes and features related to the architectures of the machine learning models, enabling the system to provide text or image outputs related to biological conditions, such as the presence of proteins or genomic regions indicative of cancer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative machine learning models are coupled with imaging data analysis systems, then the comprehensiveness of biological condition detection is improved, but the processing time and resource consumption increase
Solution Approach 1:
The patent segments the machine learning model into specialized components: a vision encoder for imaging data, a language encoder for medical reports, and a projection layer for feature alignment. This segmentation allows parallel processing of different data types and reduces the computational burden on any single component, thereby maintaining comprehensive analysis capability while reducing processing time.
Solution Approach 2:
The patent implements preliminary action by pre-processing imaging data into patch embeddings and pre-training the vision encoder on large-scale medical image datasets before fine-tuning on specific tasks. This pre-computation of feature representations enables faster inference time during actual diagnostic operations while maintaining comprehensive detection capability.
2Measurement precision
If generative machine learning models are used for comprehensive biological analysis, then the detection accuracy is improved, but the memory resources required increase
Solution Approach 1:
The patent extracts only the essential features from imaging data by using a vision encoder that processes images into compact patch embedding representations. This extraction approach maintains high detection accuracy by preserving critical diagnostic features while reducing the dimensionality and memory footprint of the data, eliminating redundant information that would consume excessive memory resources.
Solution Approach 2:
The patent applies local quality by processing imaging data in localized patches rather than treating the entire image as a single unit. Each patch is independently encoded and then aggregated, allowing the system to focus computational and memory resources on locally relevant diagnostic features while maintaining overall detection accuracy through the aggregation of patch-level analyses.
3Adaptability or versatility
If complex machine learning architectures are implemented, then the analysis capability is improved, but the training speed decreases
Solution Approach 1:
The patent merges the vision encoder and language encoder through a projection layer that aligns their feature spaces, enabling joint training on multi-modal medical data. This merging allows the model to learn synergistic representations from both imaging and textual data simultaneously, improving overall analysis capability while maintaining training efficiency through unified optimization rather than separate training pipelines.
Solution Approach 2:
The patent employs parameter changes by implementing progressive freezing of encoder parameters during training. Initially, all parameters are trained to enable comprehensive analysis capability development. As training progresses, certain encoder parameters are frozen to reduce the number of trainable parameters, thereby increasing training speed in later stages while preserving the analysis capabilities learned during the full-training phase.
Data Source
AI summary
Implementations described herein are directed to the analysis of biological images to identify subjects in which a biological condition is present. The biological images can be analyzed by implementing one or more machine learning techniques. For example, one or more transformer models can be implemented to analyze biological images to identify subjects in which a biological condition is present. In one or more examples, chat operations can be integrated with the analysis of biological images to access various types of functionalities that can be performed with respect to the analysis of biological images, such as identifying images that are indicative of a biological condition, generating reports based on the image analysis, and navigating workflows related to the analysis of biological images.