Annotation-Efficient Deep Learning for Medical Imaging
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Solution Overview
Problem
The challenge in medical imaging is the tedious and time-consuming process of annotating medical images, which requires costly, specialty-oriented knowledge and skills, and is prone to misdiagnosis due to the complexity of anatomical structures and abnormalities.
Innovation Solution
The development of annotation-efficient deep learning methodologies that utilize active selection patterns, multi-scale feature aggregation, and generic image representation to reduce the need for extensive annotation, allowing deep models to approximate or outperform prior models with limited annotated data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive annotation of medical images is performed to train deep learning models, then model performance and accuracy are improved, but annotation costs and time consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by using the deep learning model to pre-annotate medical images before human expert review. This pre-annotation step prepares the data in advance, reducing the time required for manual annotation while maintaining model performance through subsequent expert verification.
Solution Approach 2:
The deep learning model serves itself by generating its own training data through pre-annotation of medical images. The model's predictions are used as preliminary labels that reduce the burden on human annotators, allowing the system to partially annotate data autonomously while improving efficiency.
2Measurement precision
If extensive annotation of medical images is performed to train deep learning models, then model performance and accuracy are improved, but annotation costs increase due to requirement of specialty knowledge
Solution Approach 1:
The deep learning model autonomously performs pre-annotation of medical images, reducing the need for expensive human expert annotation. The model generates preliminary labels that require minimal human verification, significantly lowering annotation costs while maintaining performance through selective expert review of uncertain cases.
Solution Approach 2:
The system extracts only the most uncertain or ambiguous predictions from the deep learning model for human expert review. By taking out only these critical cases requiring specialty knowledge, the system minimizes the portion of annotation work that requires expensive experts while maintaining overall model performance.
3Measurement precision
If manual annotation by specialty experts is performed, then diagnostic accuracy is improved, but workload burden on medical professionals increases
Solution Approach 1:
The deep learning model performs self-service by automatically pre-annotating medical images, thereby reducing the workload burden on medical professionals. The model handles the bulk of annotation work autonomously, allowing experts to focus only on verifying and correcting predictions, which improves productivity while maintaining diagnostic accuracy.
Solution Approach 2:
The system extracts and presents only the most challenging or uncertain cases to medical professionals for review. By taking out these specific cases that require expert judgment, the system minimizes the overall workload burden while preserving diagnostic accuracy for complex cases that truly need human expertise.
Data Source
AI summary
Embodiments described herein include systems for implementing annotation-efficient deep learning in computer-aided diagnosis. Exemplary embodiments include systems having a processor and a memory specially configured with instructions for learning annotation-efficient deep learning from non-labeled medical images to generate a trained deep-learning model by applying a multi-phase model training process via specially configured instructions for pre-training a model by executing a one-time learning procedure using an initial annotated image dataset; iteratively re-training the model by executing a fine-tuning learning procedure using newly available annotated images without re-using any images from the initial annotated image dataset; selecting a plurality of most representative samples related to images of the initial annotated image dataset and the newly available annotated images by executing an active selection procedure based on the which of a collection of un-annotated images exhibit either a greatest uncertainty or a greatest entropy; extracting generic image features; updating the model using the generic image features extracted; and outputting the model as the trained deep-learning model for use in analyzing a patient medical image. Other related embodiments are disclosed.