Self-Supervised Feature Encoder for Mammogram Analysis
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Solution Overview
Problem
Existing deep learning-based methods for predicting breast diseases from mammogram images require large amounts of manually annotated data, making it challenging to train effective models due to the time-consuming nature of data annotation.
Innovation Solution
A system that uses a feature encoder trained on multi-view mammogram images from different persons to extract features, minimizing differences between features from the same person and maximizing differences between features from different persons, thereby predicting abnormal breast conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based methods are used to predict breast diseases from mammogram images, then detection accuracy can be improved, but the requirement for large amounts of manually annotated data increases training complexity and time consumption
Solution Approach 1:
The patent applies preliminary action by pre-training the feature encoder on large amounts of unlabeled mammogram images before the actual disease detection task. This pre-training stage learns general breast tissue representations and anatomical structures, so that when the model encounters labeled data during fine-tuning, it already possesses useful feature extraction capabilities, thereby reducing the amount of labeled data needed and simplifying the overall training process
Solution Approach 2:
The patent implements self-service through self-supervised learning where the model trains on its own outputs. The feature encoder processes unlabeled images and creates its own training signals by comparing features from different views of the same breast, eliminating the need for external manual annotation. This self-service approach allows the model to learn meaningful representations without requiring human experts to annotate each training sample
2Reliability
If manually annotated data is used for training deep learning models, then model performance can be optimized, but data annotation becomes difficult and time consuming
Solution Approach 1:
The patent implements self-service through self-supervised learning where the model trains on its own outputs. The feature encoder processes unlabeled images and creates its own training signals by comparing features from different views of the same breast, eliminating the need for external manual annotation. This self-service approach allows the model to learn meaningful representations without requiring human experts to annotate each training sample
Solution Approach 2:
The patent applies copying by creating synthetic training data through image transformations and view synthesis. The system generates artificial training examples by transforming existing mammogram images (through rotation, flipping, and other augmentations) and by synthesizing multi-view images from single-view inputs, thereby copying and replicating training data without requiring new annotated samples from radiologists
3Measurement precision
If feature encoder is trained to minimize differences between image features from the same person and maximize differences between features from different persons, then feature discrimination capability is improved, but training process becomes more complex
Solution Approach 1:
The patent applies preliminary action by pre-training the feature encoder on large amounts of unlabeled mammogram images before the actual disease detection task. This pre-training stage learns general breast tissue representations and anatomical structures, so that when the model encounters labeled data during fine-tuning, it already possesses useful feature extraction capabilities, thereby reducing the amount of labeled data needed and simplifying the overall training process
Solution Approach 2:
The patent implements feedback through contrastive loss functions that provide gradient-based feedback during training. The loss function compares features from different views of the same breast (which should be similar) with features from different breasts (which should be different), and adjusts the encoder parameters accordingly. This feedback mechanism automatically optimizes feature discrimination without requiring manual intervention or complex training procedures
Data Source
AI summary
Described herein are systems, methods, and instrumentalities associated with processing mammogram images using machine learning based techniques. An apparatus as described herein may obtain a mammographic image of a person, extract features from the mammographic image using a feature encoder, and predict a health condition of the person based on the extracted features. The feature encoder may be trained using a self-supervised technique and based on multi-view mammogram images that may belong to a same person or to different people.