https://github.com/uni-medical/imis-bench
공식 코드Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
Papers with Code 아카이브
Medical Image Segmentation
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Medical Image Segmentation is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.
<span style="color:grey; opacity: 0.6">( Image credit: IVD-Net )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/uni-medical/sam-med3d
공식 코드SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images
https://github.com/urrand/perinatal-pipeline
공식 코드An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis
https://github.com/usagisukisuki/adaptive_t-vmf_dice_loss
공식 코드Adaptive t-vMF Dice Loss for Multi-class Medical Image Segmentation
https://github.com/utkuozbulak/adaptive-segmentation-mask-attack
공식 코드Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation
https://github.com/vamzimmer/multitask_seg_placenta
공식 코드https://github.com/vanvalenlab/deepcell-tf
공식 코드https://github.com/verena-hallitschke/pet-ct-annotate
공식 코드https://github.com/vibashan/tt-sfuda
공식 코드Target and Task specific Source-Free Domain Adaptive Image Segmentation
https://github.com/vietthanh2710/ac-mambaseg
공식 코드https://github.com/vinairesearch/3d-ucaps
공식 코드3D-UCaps: 3D Capsules Unet for Volumetric Image Segmentation
https://github.com/vinairesearch/point-unet
공식 코드Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation
https://github.com/vinhhust2806/ADSNet
공식 코드Adaptation of Distinct Semantics for Uncertain Areas in Polyp Segmentation
https://github.com/vinhhust2806/Polyp-SES
공식 코드Polyp-SES: Automatic Polyp Segmentation with Self-Enriched Semantic Model
https://github.com/vinloo/u-net-receptive-field-study
공식 코드Demystifying the Effect of Receptive Field Size in U-Net Models for Medical Image Segmentation
https://github.com/vinyehshaw/hff
공식 코드Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective
https://github.com/vios-s/DGNet
공식 코드Semi-supervised Meta-learning with Disentanglement for Domain-generalised Medical Image Segmentation
https://github.com/vios-s/diff-scm
공식 코드What is Healthy? Generative Counterfactual Diffusion for Lesion Localization
https://github.com/vios-s/multimodal_segmentation
공식 코드Disentangle, align and fuse for multimodal and semi-supervised image segmentation
https://github.com/vios-s/vmfnet
공식 코드vMFNet: Compositionality Meets Domain-generalised Segmentation
https://github.com/virgil3706/memory-efficient-u-net
공식 코드Memory-efficient Segmentation of High-resolution Volumetric MicroCT Images
https://github.com/virginiafdez/brainspade3d_rel
공식 코드A 3D generative model of pathological multi-modal MR images and segmentations
https://github.com/vlbthambawita/divergent-nets
공식 코드DivergentNets: Medical Image Segmentation by Network Ensemble
https://github.com/vlbthambawita/pyra-pytorch
공식 코드Pyramid-Focus-Augmentation: Medical Image Segmentation with Step-Wise Focus
https://github.com/vlbthambawita/singan-seg-polyp
공식 코드SinGAN-Seg: Synthetic training data generation for medical image segmentation
https://github.com/vpulab/med-sam-brain
공식 코드https://github.com/vqdang/hover_net
공식 코드HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
https://github.com/vqdang/xy_net
공식 코드HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
https://github.com/vribeiro1/skin-lesion-segmentation-agreement
공식 코드Handling Inter-Annotator Agreement for Automated Skin Lesion Segmentation
https://github.com/vuptran/cardiac-segmentation
공식 코드A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI
https://github.com/vvjia/sam-swin
공식 코드https://github.com/wangangcheng/lora-pt
공식 코드https://github.com/wanghr64/weakmedsam
공식 코드https://github.com/wangn123/missu
공식 코드MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet
https://github.com/wangsssky/MedicalMatting
공식 코드Medical Matting: A New Perspective on Medical Segmentation with Uncertainty
https://github.com/wasserth/totalsegmentator
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.