Specialized Machine Learning Model for Cancer Detection
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Solution Overview
Problem
Current machine learning systems face challenges in accurately predicting cancer characteristics due to the limited size of datasets from clinical trials, leading to overfitting and inaccurate predictions.
Innovation Solution
A system and method for generating a specialized machine learning model by modifying a generalized pan-cancer machine learning model using a small dataset from clinical trials or rare diseases, leveraging low-shot learning techniques to adapt the model for specific tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If deep learning and machine learning techniques are applied to small datasets from clinical trials, then the system can process limited data, but the model suffers from overfitting and produces inaccurate predictions
Solution Approach 1:
The patent segments the machine learning pipeline into distinct modules: image processing module, feature extraction module, and classification module. This segmentation allows each component to be optimized independently and facilitates better generalization from small datasets by preventing any single component from overfitting to the limited training data.
Solution Approach 2:
The patent applies preliminary actions by pre-processing images and extracting features before the final classification. The system performs preliminary image processing, normalization, and feature extraction using transfer learning models that have been pre-trained on large datasets, thereby preparing the data in advance to reduce overfitting during the actual classification task.
Solution Approach 3:
The patent introduces an intermediary feature extraction layer that acts as a mediator between the input images and the classification output. This intermediary layer uses transfer learning techniques to extract meaningful features without directly training on the small dataset, thereby preventing overfitting while maintaining prediction accuracy.
2Adaptability or versatility
If a specialized machine learning model is trained from scratch using small clinical trial data, then the model can be tailored to specific tasks, but the training process is inefficient and prone to overfitting
Solution Approach 1:
The patent performs preliminary training using transfer learning with pre-trained models on large datasets before fine-tuning on the small clinical trial data. This preliminary action allows the model to learn general features efficiently and then adapt to specific tasks without requiring retraining from scratch, thereby improving training efficiency while maintaining specialization.
Solution Approach 2:
The patent uses copying by initializing the specialized model weights with the pre-trained model weights. This copying approach allows the specialized model to inherit the learned features from the pre-trained model, enabling efficient adaptation to specific tasks without requiring extensive retraining, thus improving productivity while maintaining adaptability.
3Adaptability or versatility
If a generalized pan-cancer machine learning model is used, then the model can handle diverse cancer types, but the model lacks specificity for particular cancer characteristics and treatments
Solution Approach 1:
The patent segments the model into a generalized pan-cancer backbone and task-specific output layers. The backbone handles diverse cancer types generally, while the output layers can be specialized for specific cancer characteristics or treatments. This segmentation allows the model to maintain pan-cancer capability while achieving precise detection for specific tasks through modular customization.
Solution Approach 2:
The patent applies local quality by maintaining a generalized model architecture while allowing the output layer to be customized for specific cancer types or characteristics. The model structure remains general for handling diverse cancers, but the local output layer is optimized for specific measurement precision requirements, thereby achieving both versatility and accuracy.
Data Source
AI summary
Systems and methods are disclosed for generating a specialized machine learning model by receiving a generalized machine learning model generated by processing a plurality of first training images to predict at least one cancer characteristic, receiving a plurality of second training images, the first training images and the second training images include images of tissue specimens and/or images algorithmically generated to replicate tissue specimens, receiving a plurality of target specialized attributes related to a respective second training image of the plurality of second training images, generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of second training images and the target specialized attributes, receiving a target image corresponding to a target specimen, applying the specialized machine learning model to the target image to determine at least one characteristic of the target image, and outputting the characteristic of the target image.