Skin Lesion Classification
36 papers with code • 1 benchmarks • 7 datasets
HAM10000
MSK
BCN_20000
SD-198
Latest papers
Association Graph Learning for Multi-Task Classification with Category Shifts
autumn9999/mtc-with-category-shifts
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To generalize to such test data, it is crucial for individual tasks to leverage knowledge from related tasks.
CIRCLe: Color Invariant Representation Learning for Unbiased Classification of Skin Lesions
While deep learning based approaches have demonstrated expert-level performance in dermatological diagnosis tasks, they have also been shown to exhibit biases toward certain demographic attributes, particularly skin types (e. g., light versus dark), a fairness concern that must be addressed.
FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning
Deep learning models have achieved great success in automating skin lesion diagnosis.
Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion Images
Through this approach, 1) First, we target the mixup amongst middle and tail classes to address the long-tail problem.
Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification
adriano-lucieri/shape-bias-in-dermoscopy
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Interestingly, despite deep feature extractors being inclined towards learning entangled features for skin lesion classification, individual features can still be decoded from this entangled representation.
SSD-KD: A Self-supervised Diverse Knowledge Distillation Method for Lightweight Skin Lesion Classification Using Dermoscopic Images
enkiwang/portable-skin-lesion-diagnosis
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In this case, knowledge distillation (KD) has been proven as an efficient tool to help improve the adaptability of lightweight models under limited resources, meanwhile keeping a high-level representation capability.
Deep Multimodal Guidance for Medical Image Classification
Furthermore, in the case of brain tumor classification, our method outperforms the model trained on the superior modality while producing comparable results to the model that uses both modalities during inference.
Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification
pbevan1/detecting-melanoma-fairly
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Convolutional Neural Networks have demonstrated human-level performance in the classification of melanoma and other skin lesions, but evident performance disparities between differing skin tones should be addressed before widespread deployment.
FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis
However, designing a unified BA method that can be applied to various MIA systems is challenging due to the diversity of imaging modalities (e. g., X-Ray, CT, and MRI) and analysis tasks (e. g., classification, detection, and segmentation).
Fair Conformal Predictors for Applications in Medical Imaging
clu5/aaai22
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Deep learning has the potential to automate many clinically useful tasks in medical imaging.
