Balanced Few-Shot Learning Accurately Diagnoses Retinal Disease with Limited Data, Addressing Imbalance in 1,000 Test Images

Automated diagnosis of retinal diseases, such as diabetic retinopathy and macular degeneration, is increasingly important given their rising prevalence, yet current deep learning methods often struggle with limited and imbalanced datasets. Jasmaine Khale from Northeastern University-Silicon Valley and Ravi Prakash Srivastava from the Indian Institute of Information Technology, Ranchi, address this challenge by developing a new approach to few-shot learning, enabling accurate diagnosis from only a few labelled images per disease. Their research introduces a balanced framework that specifically tackles the issue of imbalanced datasets in retinal imaging, ensuring all disease categories contribute equally to the learning process. By combining balanced sampling techniques with targeted image augmentation and a powerful image analysis system, the team achieves significant improvements in diagnostic accuracy and reduces bias towards more common conditions, offering a promising path towards more robust and equitable clinical tools for detecting a range of retinal diseases.

Acquiring large, annotated medical image datasets is often expensive and time-consuming, so the team developed a system that learns effectively from only a few examples of each disease. The method utilizes a Prototypical Network, which learns to represent images in a way that groups similar diseases together and separates different ones. This allows the system to classify new images by comparing them to representative examples, or prototypes, of each disease.

The training process simulates real-world scenarios where only a few examples are available for each disease. By structuring the training around these “episodes,” the model learns to generalize from limited data and avoid bias towards more common conditions. A key innovation is the emphasis on balancing these episodes, ensuring each disease is represented equally to prevent the model from favoring prevalent conditions. The team also employs data augmentation techniques, including Contrast Limited Adaptive Histogram Equalization (CLAHE), to improve image contrast and highlight subtle features indicative of disease.

The results demonstrate improved performance in classifying retinal diseases with limited data compared to conventional methods. Balanced episodic training proves crucial for preventing bias and accurately diagnosing rarer diseases. The use of CLAHE further enhances performance by improving image quality and feature extraction. While the model still faces challenges in differentiating between visually similar conditions, the research represents a significant step towards developing clinically reliable diagnostic tools. This work contributes a novel application of Prototypical Networks to retinal disease classification, highlighting the importance of balanced episodic training for mitigating bias and improving the diagnosis of minority diseases. The effective use of CLAHE as a data augmentation technique further enhances image quality and feature extraction.

Balanced Episodic Learning Improves Retinal Diagnosis

This research presents a balanced few-shot episodic learning framework designed to improve the accuracy and fairness of retinal disease diagnosis. Recognizing the limitations of conventional deep learning methods, which require extensive labeled data, the team developed a system capable of generalizing from a small number of examples per disease category. The framework integrates balanced episodic sampling, ensuring all diseases are represented equally during training, with targeted data augmentation techniques, specifically Contrast Limited Adaptive Histogram Equalization (CLAHE), to enhance the diversity of minority classes. The research focuses on the Retinal Fundus Multi-Disease Image Dataset (RFMiD), addressing the inherent imbalance in the distribution of cases.

The core of the method involves constructing episodes during training, each comprising a scenario where the system learns to classify images based on only a few examples per disease. Crucially, the team implemented balanced episodic sampling, ensuring equal participation of all diseases within each episode to prevent the model from favoring more frequent conditions. To further enhance the representation of minority classes, targeted data augmentation techniques, including CLAHE and geometric transformations, were employed, increasing the diversity of training images and improving the visibility of subtle retinal features. A ResNet-50 encoder, pre-trained on the ImageNet dataset, effectively captures these fine-grained details.

Experiments demonstrate substantial improvements in classification performance through balanced episodic sampling, which ensures equal representation of all classes during training. This effectively counteracts the tendency of models to favor more frequent diseases, such as Diabetic Retinopathy, while also improving performance on rarer conditions. Targeted augmentation, including CLAHE, further enhances the visibility of fine-grained retinal features, improving the model’s ability to distinguish between subtle differences in disease presentation. This research highlights the potential of dataset-aware few-shot learning strategies for building more robust and clinically fair diagnostic systems in ophthalmology.

Few-Shot Learning Improves Retinal Disease Diagnosis

This research introduces a novel few-shot learning framework designed to improve the accuracy and fairness of automated retinal disease diagnosis. Recognizing the limitations of conventional deep learning methods, which require extensive labeled data, the team developed a system capable of generalizing from a small number of examples per disease category. The framework integrates balanced episodic sampling, ensuring all diseases are represented equally during training, with targeted data augmentation techniques, specifically Contrast Limited Adaptive Histogram Equalization (CLAHE), to enhance the diversity of minority classes. The research focuses on the ten most represented disease categories within the Retinal Fundus Multi-Disease Image Dataset (RFMiD).

The core of the method involves constructing episodes during training, each comprising a scenario where the system learns to classify images based on only a few examples per disease. Crucially, the team implemented balanced episodic sampling, ensuring equal participation of all diseases within each episode to prevent the model from favoring more frequent conditions. To further enhance the representation of minority classes, targeted data augmentation techniques, including CLAHE, were employed, increasing the diversity of training images and improving the visibility of subtle retinal features. A ResNet-50 encoder, pre-trained on the ImageNet dataset, effectively captures these fine-grained details. Experiments demonstrate substantial improvements in diagnostic accuracy, particularly for underrepresented retinal diseases, and a reduction in bias toward more prevalent conditions. By combining these techniques with a ResNet-50 encoder, the team achieved a robust and clinically relevant system capable of performing well even with limited data.