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://github.com/Merrical/PADL

공식 코드

Modeling Annotator Preference and Stochastic Annotation Error for Medical Image Segmentation

https://github.com/Merrical/TAB

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Transformer-based Annotation Bias-aware Medical Image Segmentation

https://github.com/MiguelMonteiro/CRFasRNNLayer

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Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation

https://github.com/MiguelMonteiro/permutohedral_lattice

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Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation

https://github.com/MinaJf/DRU-net

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DRU-net: An Efficient Deep Convolutional Neural Network for Medical Image Segmentation

https://github.com/MittaDhanunjaya/Master-Thesis

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Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation

https://github.com/MoPl90/image_translation

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Image Translation for Medical Image Generation -- Ischemic Stroke Lesions

https://github.com/MrGiovanni/ModelsGenesis

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Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

https://github.com/MrGiovanni/Nested-UNet

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UNet++: A Nested U-Net Architecture for Medical Image Segmentation

https://github.com/MrGiovanni/UNetPlusPlus

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UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

https://github.com/Murtadha44/-Intracranial-Hemorrhage-Segmentation-Using-Deep-Convolutional-Model-U-Net-

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Intracranial Hemorrhage Segmentation Using Deep Convolutional Model

https://github.com/NIC-VICOROB/SUNet-architecture

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Acute and sub-acute stroke lesion segmentation from multimodal MRI

https://github.com/NIC-VICOROB/cnn-ms-lesion-segmentation

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Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach

https://github.com/NIC-VICOROB/nicmslesions

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One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks

https://github.com/NIC-VICOROB/stroke-mri-segmentation

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Acute and sub-acute stroke lesion segmentation from multimodal MRI

https://github.com/NIC-VICOROB/tissue_segmentation_comparison

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Quantitative analysis of patch-based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging

https://github.com/NVIDIA/DeepLearningExamples

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Optimized U-Net for Brain Tumor Segmentation

https://github.com/NYUMedML/DARTS

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DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation

https://github.com/NoviceMAn-prog/GMSRFNet

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GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation

https://github.com/NoviceMAn-prog/MSRF-Net

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MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation

https://github.com/Nuisal/cellseg1

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CellSeg1: Robust Cell Segmentation with One Training Image

https://github.com/Omid-Nejati/Dilated_Unet

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Dilated-UNet: A Fast and Accurate Medical Image Segmentation Approach using a Dilated Transformer and U-Net Architecture

https://github.com/OpenGVLab/SAM-Med2D

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https://github.com/Pablo1990/3D-deep-segmentation-protocol

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Single cell resolution 3D imaging and segmentation within intact live tissues

https://github.com/PaddlePaddle/Research

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Robust Retinal Vessel Segmentation from a Data Augmentation Perspective

https://github.com/PengyiZhang/ACCL

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ACCL: Adversarial constrained-CNN loss for weakly supervised medical image segmentation

https://github.com/Powertony102/corn_official

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Leveraging CORAL-Correlation Consistency Network for Semi-Supervised Left Atrium MRI Segmentation

https://github.com/Precision-Medical-Imaging-Group/BraTS2023-inferCode

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Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging

https://github.com/Precision-Medical-Imaging-Group/BraTS2024-PEDS

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Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation

https://github.com/Project-MONAI/MONAI

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MONAI: An open-source framework for deep learning in healthcare

https://github.com/Project-MONAI/VISTA

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VISTA3D: Versatile Imaging SegmenTation and Annotation model for 3D Computed Tomography

https://github.com/Project-MONAI/research-contributions/tree/master/SwinUNETR

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Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis

https://github.com/Project-MONAI/research-contributions/tree/master/SwinUNETR/BRATS21

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Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

https://github.com/Project-MONAI/research-contributions/tree/master/UNETR/BTCV

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UNETR: Transformers for 3D Medical Image Segmentation

https://github.com/Project-MONAI/tutorials/tree/master/automl/DiNTS

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DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation

https://github.com/QQTh4/NAS4Ultrasound

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SSHNN: Semi-Supervised Hybrid NAS Network for Echocardiographic Image Segmentation

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