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Skin

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/hectorcarrion/fedd

공식 코드

FEDD -- Fair, Efficient, and Diverse Diffusion-based Lesion Segmentation and Malignancy Classification

https://github.com/hritam-98/MFSNet

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MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation

https://github.com/j05t/lesion-analysis

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Analysis of skin lesion images with deep learning

https://github.com/jacobzhaoziyuan/SAAL

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Self-supervised Assisted Active Learning for Skin Lesion Segmentation

https://github.com/jcruan519/ege-unet

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EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation

https://github.com/jcruan519/malunet

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MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation

https://github.com/jcwang123/BA-Transformer

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Boundary-aware Transformers for Skin Lesion Segmentation

https://github.com/jcwang123/xboundformer

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XBound-Former: Toward Cross-scale Boundary Modeling in Transformers

https://github.com/jingyechen/mt-transunet

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MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification

https://github.com/jwicaksana/fca

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FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring

https://github.com/jwicaksana/fedmix

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FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation

https://github.com/kakumarabhishek/Illumination-based-Transformations-Skin-Lesion-Segmentation

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Illumination-based Transformations Improve Skin Lesion Segmentation in Dermoscopic Images

https://github.com/kakumarabhishek/MCC-Loss

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Matthews Correlation Coefficient Loss for Deep Convolutional Networks: Application to Skin Lesion Segmentation

https://github.com/kamruleee51/Skin-Lesion-Segmentation-Using-Proposed-DSNet

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DSNet: Automatic Dermoscopic Skin Lesion Segmentation

https://github.com/learningtitans/cvpr-skin-solo-ensemble

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Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification

https://github.com/learningtitans/isbi2017-part1

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RECOD Titans at ISIC Challenge 2017

https://github.com/learningtitans/isbi2017-part3

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RECOD Titans at ISIC Challenge 2017

https://github.com/longman-stan/skindistilvit

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SkinDistilViT: Lightweight Vision Transformer for Skin Lesion Classification

https://github.com/luo-lorry/Stylized-Medical-Segmentation

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Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation

https://github.com/masih4/Skin-lesion-segmentation-effects-of-the-classification-perfromnce

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The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification

https://github.com/mayurmallya/deepguide

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Deep Multimodal Guidance for Medical Image Classification

https://github.com/miltonbd/ISIC_2018_classification

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Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge

https://github.com/mindflow-institue/daeformer

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DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation

https://github.com/mindflow-institue/dermosegdiff

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DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation

https://github.com/mindflow-institue/transception

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Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach

https://github.com/mindflow-institue/waveformer

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Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers

https://github.com/nabsabraham/focal-tversky-unet

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A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

https://github.com/ngessert/patch-lesion

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Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting

https://github.com/nitr098/attswinunet

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Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion Segmentation

https://github.com/nourhanb/biaspruner

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BiasPruner: Debiased Continual Learning for Medical Image Classification

https://github.com/nqnguyen812/mambau-lite

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MambaU-Lite: A Lightweight Model based on Mamba and Integrated Channel-Spatial Attention for Skin Lesion Segmentation

https://github.com/obsinaan/detecting-melanoma-fairly

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Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification

https://github.com/osupcvlab/mobileunetr

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MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation

https://github.com/pbevan1/detecting-melanoma-fairly

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Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification

https://github.com/pooya-mohammadi/unet-skin-cancer

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U-Net-based Models for Skin Lesion Segmentation: More Attention and Augmentation

https://github.com/raumannsr/ENHANCE

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ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification

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