Papers with Code 아카이브

Medical Image Segmentation

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하위 태스크: 31
평가표: 49
보존된 평가 기록: 332

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://bitbucket.org/bchradiology/u-net

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Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning

https://bitbucket.org/t_scherr/cell-segmentation-and-tracking

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Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy

https://bitbucket.org/woalsdnd/v-gan

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Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks

https://git.fmrib.ox.ac.uk/vaanathi/truenet_tumseg

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Brain tumour segmentation using a triplanar ensemble of U-Nets

https://git.rwth-aachen.de/bcs/projects/tp/multiclass-yeast-seg

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Analysis of the performance of U-Net neural networks for the segmentation of living cells

https://git.scc.kit.edu/kit-loe-ge/embedtrack

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EmbedTrack -- Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths

https://github.55860.com/perceivelab/panknet

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Hierarchical 3D Feature Learning for Pancreas Segmentation

https://github.com/0liliulei/Mem3D

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Quality-Aware Memory Network for Interactive Volumetric Image Segmentation

https://github.com/11yxk/sam-lst

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Ladder Fine-tuning approach for SAM integrating complementary network

https://github.com/15bonte/isl_segmentation

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Learning with minimal effort: leveraging in silico labeling for cell and nucleus segmentation

https://github.com/2760613195/km_unet

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KM-UNet KAN Mamba UNet for medical image segmentation

https://github.com/282857341/nnformer

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nnFormer: Interleaved Transformer for Volumetric Segmentation

https://github.com/6zrg/quantitative-statistics-of-fccm

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Innovative Quantitative Analysis for Disease Progression Assessment in Familial Cerebral Cavernous Malformations

https://github.com/920232796/nestedformer

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NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation

https://github.com/920232796/setr-pytorch

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ICOS Protein Expression Segmentation: Can Transformer Networks Give Better Results?

https://github.com/AAleka/Cycle-CBAM-and-CBAM-UNet/tree/main/UNet

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Retinal Image Restoration and Vessel Segmentation using Modified Cycle-CBAM and CBAM-UNet

https://github.com/AI-in-Cardiovascular-Medicine/nnUZoo

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From Claims to Evidence: A Unified Framework and Critical Analysis of CNN vs. Transformer vs. Mamba in Medical Image Segmentation

https://github.com/AIEMMU/MRI_Prostate

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Deep learning in magnetic resonance prostate segmentation: A review and a new perspective

https://github.com/AIGeeksGroup/MediAug

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MediAug: Exploring Visual Augmentation in Medical Imaging

https://github.com/AKuzina/DWP

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Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems

https://github.com/AMiiR-S/Inceptnet_cancer_recognition

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INCEPTNET: Precise And Early Disease Detection Application For Medical Images Analyses

https://github.com/AfifaKhaled/Two-Independent-Teachers-are-Better-Role-Model

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Two Independent Teachers are Better Role Model

https://github.com/AnandK27/GS-TransUNet

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GS-TransUNet: Integrated 2D Gaussian Splatting and Transformer UNet for Accurate Skin Lesion Analysis

https://github.com/Andrewsher/X-Net

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X-Net: Brain Stroke Lesion Segmentation Based on Depthwise Separable Convolution and Long-range Dependencies

https://github.com/AngeLouCN/CFPNet-Medicine

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CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation

https://github.com/AngeLouCN/CaraNet

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CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects

https://github.com/AngeLouCN/DC-UNet

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DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation

https://github.com/Arksyd96/synthesis-with-slice-based-ldm

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3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes

https://github.com/Asunatan/LM-Net

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LM-Net: A Light-weight and Multi-scale Network for Medical Image Segmentation

https://github.com/Au3C2/Generator-Versus-Segmentor

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Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis

https://github.com/BBillot/SynthSeg

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A Learning Strategy for Contrast-agnostic MRI Segmentation

https://github.com/BCV-Uniandes/ROG

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Towards Robust General Medical Image Segmentation

https://github.com/BPYap/Cut-Paste-Consistency

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Cut-Paste Consistency Learning for Semi-Supervised Lesion Segmentation

https://github.com/BRML/CNNbasedMedicalSegmentation

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CNN-based Segmentation of Medical Imaging Data

https://github.com/Bala93/Context-aware-segmentation

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A context based deep learning approach for unbalanced medical image segmentation

https://github.com/Bala93/Multi-task-deep-network

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Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation

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