https://github.com/HazyResearch/model-patching
공식 코드Model Patching: Closing the Subgroup Performance Gap with Data Augmentation
Papers with Code 아카이브
Skin Cancer Classification
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/KyleYoung1997/DNNorDermatologist
공식 코드https://github.com/alceubissoto/gan-skin-lesion
공식 코드https://github.com/ayushroy2001/wagf-fusion
공식 코드A Wavelet Guided Attention Module for Skin Cancer Classification with Gradient-based Feature Fusion
https://github.com/catai9/cancer-net-sca-synth
공식 코드https://github.com/fabioperez/skin-data-augmentation
공식 코드https://github.com/hasibzunair/adversarial-lesions
공식 코드Melanoma Detection using Adversarial Training and Deep Transfer Learning
https://github.com/longman-stan/skindistilvit
공식 코드SkinDistilViT: Lightweight Vision Transformer for Skin Lesion Classification
https://github.com/pbevan1/Skin-Deep-Unlearning
공식 코드Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification
https://github.com/skrantidatta/Attention-based-Skin-Cancer-Classification
공식 코드Soft-Attention Improves Skin Cancer Classification Performance
https://github.com/tbdair/fedperlv1.0
공식 코드Semi-Supervised Federated Peer Learning for Skin Lesion Classification
https://github.com/tkalbl/revisitingskintonefairness
공식 코드Revisiting Skin Tone Fairness in Dermatological Lesion Classification
https://github.com/yhygao/Efficient_Data_Augmentation
공식 코드Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training
https://github.com/CristianLazoQuispe/skin-lesion-segmentation-using-pix2pix
https://github.com/learningtitans/data-depth-design
Knowledge Transfer for Melanoma Screening with Deep Learning
https://github.com/learningtitans/isbi2017-part3
Knowledge Transfer for Melanoma Screening with Deep Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.