Fuzzy Deep Learning for Low-Shot Knee Injury Classification
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Solution Overview
Problem
Deep learning models for image classification, particularly in medical imaging, face challenges due to their data-hungry nature and reliance on large amounts of labeled data, which is scarce and costly to obtain, especially in low-data settings like medical imaging where multi-view images require efficient classification.
Innovation Solution
A processor-implemented method using a fuzzy deep learning architecture with convolutional blocks, fuzzy layers, and a classification head that iteratively performs training, pseudo-label generation, filtering, augmentation, and fine-tuning to enhance image classification performance in low-shot scenarios by leveraging both labeled and unlabeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models are used for image classification, then classification performance can be improved, but the requirement for large amounts of labeled data increases
Solution Approach 1:
The model is pre-trained on a source task with abundant data before being adapted to the target task with limited data. This preliminary training establishes strong feature extraction capabilities that transfer to the target domain, reducing the need for extensive labeled target data
Solution Approach 2:
Unlabeled target domain images serve as an intermediary resource. The model generates pseudo-labels for these unlabeled images and uses them as intermediate training signals, bridging the gap between the source task and the target task while avoiding the need for extensive manually labeled target data
2Measurement precision
If more labeled data is collected for training, then model accuracy improves, but time and cost increase
Solution Approach 1:
The model performs self-labeling by generating pseudo-labels for unlabeled target domain images. This self-service mechanism eliminates the need for manual annotation of target data, saving significant time and resources while still achieving high accuracy through iterative refinement of predictions
Solution Approach 2:
Labels from the source task are copied and adapted to the target task through transfer learning. The knowledge and patterns learned from source task labels are transferred to guide predictions on target task images, reducing the need for collecting new labeled data from scratch
3Adaptability or versatility
If traditional deep learning models are applied to multi-view medical images, then classification capability is achieved, but adaptability to low-data settings deteriorates
Solution Approach 1:
The training regime is changed from standard supervised learning to a multi-stage process involving pre-training on source task, pseudo-label generation, and iterative fine-tuning. This parameter change in the training approach enables the model to adapt to low-data settings while maintaining classification capability through effective use of both labeled and unlabeled data
Data Source
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AI summary
The present disclosure provides methods and systems for task adaptation using fuzzy deep learning architecture. In the present disclosure, a low-shot approach for knee injury classification is proposed along with a deep learning architecture utilizing a fuzzy layer. For the low-shot approach, a stage of knowledge transfer takes place from a first classification task (source task) to a second classification task (target task) through a task adaptation approach. The first classification task and the second classification task are two related diagnoses of the knee, where sufficient labeled samples are available for first classification task but very few labeled samples are available for the second classification task. Further, the trained fuzzy deep learning architecture is used to generate pseudo-labels for a collection of unlabeled samples available for and the second classification task. Then, multiple stages of fine-tuning are carried out which help in substantially improving the classification performance.