One Shot GANs for Long Tail Problem in Skin Lesion Dataset using novel content space assessment metric
This addresses the challenge of imbalanced medical image datasets for improving diagnosis models, but it is incremental as it applies an existing method with a new metric.
The paper tackled the long-tail problem in skin lesion datasets by using One Shot GANs to augment minority classes in the HAM10000 dataset, employing a novel metric to improve accuracy.
Long tail problems frequently arise in the medical field, particularly due to the scarcity of medical data for rare conditions. This scarcity often leads to models overfitting on such limited samples. Consequently, when training models on datasets with heavily skewed classes, where the number of samples varies significantly, a problem emerges. Training on such imbalanced datasets can result in selective detection, where a model accurately identifies images belonging to the majority classes but disregards those from minority classes. This causes the model to lack generalizability, preventing its use on newer data. This poses a significant challenge in developing image detection and diagnosis models for medical image datasets. To address this challenge, the One Shot GANs model was employed to augment the tail class of HAM10000 dataset by generating additional samples. Furthermore, to enhance accuracy, a novel metric tailored to suit One Shot GANs was utilized.