Properties Of Winning Tickets On Skin Lesion Classification
원문 초록
Skin cancer affects a large population every year -- automated skin cancer detection algorithms can thus greatly help clinicians. Prior efforts involving deep learning models have high detection accuracy. However, most of the models have a large number of parameters, with some works even using an ensemble of models to achieve good accuracy. In this paper, we investigate a recently proposed pruning technique called Lottery Ticket Hypothesis. We find that iterative pruning of the network resulted in improved accuracy, compared to that of the unpruned network, implying that -- the lottery ticket hypothesis can be applied to the problem of skin cancer detection and this hypothesis can result in a smaller network for inference. We also examine the accuracy across sub-groups -- created by gender and age -- and it was found that some sub-groups show a larger increase in accuracy than others.
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Fusion of Foundation and Vision Transformer Model Features for Dermatoscopic Image Classification
Uncertainty-Aware Deep Learning for Automated Skin Cancer Classification: A Comprehensive Evaluation
Skin Lesion Phenotyping via Nested Multi-modal Contrastive Learning
Domain Adaptive Skin Lesion Classification via Conformal Ensemble of Vision Transformers
LiwTERM: A Lightweight Transformer-Based Model for Dermatological Multimodal Lesion Detection
An Attention-Based Mechanism to Combine Images and Metadata in Deep Learning Models Applied to Skin Cancer Classification
토론
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