Papers with Code 아카이브

Skin Cancer Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

2

보존된 평가 기록

4

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

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

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

Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-based Non-invasive Digital System

An Interpretable Deep Learning Approach for Skin Cancer Categorization

SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model

Training on Polar Image Transformations Improves Biomedical Image Segmentation

DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation

Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks

FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation

Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation

Road Extraction by Deep Residual U-Net

U-Net: Convolutional Networks for Biomedical Image Segmentation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.