Papers with Code 아카이브
Lesion Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 9
- 보존된 평가 기록
- 42
Lesion segmentation is the task of segmenting out lesions from other objects in medical based images.
<span style="color:grey; opacity: 0.6">( Image credit: D-UNet )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jmlipman/RatLesNetv2
공식 코드RatLesNetv2: A Fully Convolutional Network for Rodent Brain Lesion Segmentation
https://github.com/jordan-colman/DR-Unet104
공식 코드https://github.com/juliawolleb/diffusion-based-segmentation
공식 코드https://github.com/jusiro/constrained_anomaly_segmentation
공식 코드https://github.com/jwicaksana/fedmix
공식 코드FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation
https://github.com/kakumarabhishek/Illumination-based-Transformations-Skin-Lesion-Segmentation
공식 코드Illumination-based Transformations Improve Skin Lesion Segmentation in Dermoscopic Images
https://github.com/kakumarabhishek/MCC-Loss
공식 코드https://github.com/kamruleee51/Skin-Lesion-Segmentation-Using-Proposed-DSNet
공식 코드https://github.com/kwfredshi/uls2023ngkd
공식 코드Universal Lesion Segmentation Challenge 2023: A Comparative Research of Different Algorithms
https://github.com/learningtitans/isbi2017-part1
공식 코드https://github.com/learningtitans/isbi2017-part3
공식 코드https://github.com/liamchalcroft/contrast-squared
공식 코드Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning
https://github.com/liamchalcroft/mdunet
공식 코드Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation
https://github.com/liamchalcroft/qsynth
공식 코드Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data
https://github.com/liamchalcroft/synthstroke
공식 코드https://github.com/liamchalcroft/synthstrokespm
공식 코드https://github.com/liu37972/Fuzzy-Seg-Deep-DuS-KFCM-
공식 코드https://github.com/liyihao76/MMAC_LaTIM_Solution
공식 코드https://github.com/luo-lorry/Stylized-Medical-Segmentation
공식 코드Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation
https://github.com/m3dv/lefusion
공식 코드LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models
https://github.com/masih4/Skin-lesion-segmentation-effects-of-the-classification-perfromnce
공식 코드The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification
https://github.com/mawady/bus-segmentation
공식 코드https://github.com/mawio02/fisrg-for-automated-lesion-after-stroke-segmentation-in-mri
공식 코드https://github.com/medcai/l2snet
공식 코드https://github.com/medical-image-analysis-laboratory/ms_wml_uncs
공식 코드https://github.com/mic-dkfz/autopet-3-submission
공식 코드https://github.com/mic-dkfz/miccai2024_autopet3_datacentric
공식 코드https://github.com/mic-dkfz/multitalent
공식 코드MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation
https://github.com/michaelvorndran/inconsistencymasks
공식 코드Inconsistency Masks: Removing the Uncertainty from Input-Pseudo-Label Pairs
https://github.com/michi-3000/fundus2video
공식 코드https://github.com/microsoft/lymphoma-segmentation-dnn
공식 코드https://github.com/mindflow-institue/daeformer
공식 코드DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation
https://github.com/mindflow-institue/dermosegdiff
공식 코드DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation
https://github.com/mindflow-institue/transception
공식 코드Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach
https://github.com/mindflow-institue/waveformer
공식 코드Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.