Annotation-Efficient Medical Imaging Analysis with Cross-Domain Learning
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Solution Overview
Problem
Supervised deep learning techniques for medical imaging are limited by the lack of annotated data, which is expensive and tedious to obtain, and there is a need for improved methods to leverage unannotated data for training machine learning algorithms.
Innovation Solution
A semi-supervised learning approach using both annotated and unannotated cross-domain medical images, where machine learning based encoding networks are jointly trained with unsupervised and supervised losses to extract and decode features for medical imaging analysis tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised deep learning techniques are used for medical imaging analysis, then accurate segmentation can be achieved, but the amount of annotated data required is limited and expensive to obtain
Solution Approach 1:
The training data is segmented into two distinct parts: a first set of annotated training images and a second set of unannotated training images. This segmentation allows the system to utilize both labeled and unlabeled data effectively, reducing dependency on expensive annotated data while maintaining segmentation accuracy through the semi-supervised learning approach.
Solution Approach 2:
A consistency regularization term acts as an intermediary mechanism that bridges the supervised and unsupervised learning components. This term enforces consistency between predictions on augmented versions of unannotated images, enabling the model to learn from unannotated data without requiring manual labels, thus resolving the contradiction between accuracy and data annotation cost.
2Measurement precision
If more annotated data is collected for training, then model accuracy improves, but the cost and time required for annotation increases
Solution Approach 1:
The system employs self-supervised learning mechanisms where the model learns from unannotated data through consistency regularization and cross-modality alignment. The model essentially serves itself by generating its own training signals from unannotated images through data augmentation and consistency constraints, eliminating the need for manual annotation while maintaining accuracy.
Solution Approach 2:
The semi-supervised learning framework serves multiple functions simultaneously: it leverages both annotated and unannotated data, performs cross-modality alignment, and applies consistency regularization. This multi-functional approach allows the system to achieve high accuracy without increasing annotation time, as the unannotated data is utilized through automated learning mechanisms.
3Ease of manufacture
If only annotated data is used for training, then training cost decreases, but the ability to leverage additional unannotated data is lost
Solution Approach 1:
The training approach is made dynamic by combining supervised learning on annotated data with unsupervised learning on unannotated data. The system adaptively utilizes both data types during training, switching between supervised and unsupervised learning modes depending on data availability, thereby reducing training costs while maximizing data utilization capability.
Solution Approach 2:
The training dataset is constructed as a composite of two distinct data types: annotated training images and unannotated training images. This composite data structure allows the model to benefit from both the labeled guidance and the volume of unlabeled data, achieving cost-effective training while maintaining high adaptability and data utilization through the semi-supervised learning framework.
Data Source
AI summary
Systems and methods for performing a medical imaging analysis task are provided. An input medical image in a first modality is received. Features are extracted from the input medical image using a first machine learning based encoding network. A medical imaging analysis task is performed on the input medical image based on the extracted features by, in one embodiment, decoding the extracted features to generate results of the medical imaging analysis task using a machine learning based decoding network. Results of the medical imaging analysis task are output. In one embodiment, the first machine learning based encoding network is jointly trained with a second machine learning based encoding network with an unsupervised loss using unannotated pairs of training images. Each of the unannotated pairs comprise a first training image in the first modality and a second training image in a second modality. In one embodiment, the first machine learning based encoding network is also jointly trained with the machine learning based decoding network with a supervised loss using annotated training images.