CNN Candidate Selection for Biomedical Image Annotation
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Solution Overview
Problem
The success of convolutional neural networks (CNNs) in biomedical image analysis is hindered by the lack of large annotated datasets, as annotating biomedical images is tedious, time-consuming, and requires costly, specialty-oriented knowledge and skills that are not easily accessible.
Innovation Solution
A system and method that selects candidates for annotation using a pre-trained CNN, determining probabilities for each patch, identifying a subset for external labeling, and continuously fine-tuning the CNN with newly annotated samples to enhance performance incrementally, integrating active learning and transfer learning into a single framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all candidates are annotated to train the CNN, then the training data quality and model performance are improved, but the annotation cost and time consumption increase significantly
Solution Approach 1:
The patent applies partial action by selecting only a subset of candidates for annotation rather than annotating all candidates. The selection is guided by uncertainty sampling, which identifies candidates that would provide the most valuable information for model improvement. This approach achieves significant performance gains while avoiding the prohibitive cost of annotating the entire dataset.
Solution Approach 2:
The system employs self-service through automated uncertainty estimation and candidate selection. The pre-trained model itself identifies which candidates it is most uncertain about, and the system automatically ranks and selects candidates for annotation without manual intervention. This reduces dependency on expert annotators and streamlines the iterative improvement process.
2Reliability
If a pre-trained CNN is used for probability determination, then the initial model performance is improved, but the need for continuous expert annotation and fine-tuning increases complexity
Solution Approach 1:
The patent applies preliminary action by pre-training the CNN on a large-scale general image dataset (such as ImageNet) before deploying it for biomedical image analysis. This pre-training provides a strong foundation that accelerates subsequent fine-tuning and reduces the amount of domain-specific annotated data needed. The pre-trained model's features are then leveraged to guide candidate selection for annotation.
Solution Approach 2:
The system implements feedback through iterative cycles where the model generates predictions, uncertainty is estimated, candidates are selected for annotation, and the model is fine-tuned on the new labeled data. This closed-loop feedback mechanism continuously improves model performance while automatically managing the complexity of the annotation process through algorithmic candidate selection.
Data Source
AI summary
Systems for selecting candidates for labelling and use in training a convolutional neural network (CNN) are provided, the systems comprising: a memory device; and at least one hardware processor configured to: receive a plurality of input candidates, wherein each candidate includes a plurality of identically labelled patches; and for each of the plurality of candidates: determine a plurality of probabilities, each of the plurality of probabilities being a probability that a unique patch of the plurality of identically labelled patches of the candidate corresponds to a label using a pre-trained CNN; identify a subset of candidates of the plurality of input candidates, wherein the subset does not include all of the plurality of candidates, based on the determined probabilities; query an external source to label the subset of candidates to produce labelled candidates; and train the pre-trained CNN using the labelled candidates.