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/rohit-kundu/ideal-icassp23

공식 코드

IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation

https://github.com/romainvala/synthetic_learning_on_dhcp

공식 코드

Comprehensive analysis of synthetic learning applied to neonatal brain MRI segmentation

https://github.com/rongtao-xu/skinformer

공식 코드

SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation

https://github.com/rongzhao-zhang/ag-crc

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AG-CRC: Anatomy-Guided Colorectal Cancer Segmentation in CT with Imperfect Anatomical Knowledge

https://github.com/rongzhou7/ttt-unet

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TTT-Unet: Enhancing U-Net with Test-Time Training Layers for Biomedical Image Segmentation

https://github.com/rosanajurdi/prior-based-losses-for-medical-image-segmentation

공식 코드

Effect of Prior-based Losses on Segmentation Performance: A Benchmark

https://github.com/rows21/dk-uxnet

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Semi-supervised Medical Image Segmentation via Query Distribution Consistency

https://github.com/roydenwa/efficient-cell-seg

공식 코드

EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring

https://github.com/royerlab/ultrack

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Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps

https://github.com/rsinghlab/gnn-tumor-seg

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A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation

https://github.com/rsummers11/CADLab

공식 코드

XLSor: A Robust and Accurate Lung Segmentor on Chest X-Rays Using Criss-Cross Attention and Customized Radiorealistic Abnormalities Generation

https://github.com/rt219/the-emergence-of-objectness

공식 코드

The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos

https://github.com/rubics-xuan/fremim

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FreMIM: Fourier Transform Meets Masked Image Modeling for Medical Image Segmentation

https://github.com/ruiyangju/fce-yolov8

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Pediatric Wrist Fracture Detection Using Feature Context Excitation Modules in X-ray Images

https://github.com/ruizhe-l/semi-segmentation

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A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation

https://github.com/runminjiang/pw4medseg

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Enhancing Weakly Supervised 3D Medical Image Segmentation through Probabilistic-aware Learning

https://github.com/ruochengao/swin_litemedsam

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Swin-LiteMedSAM: A Lightweight Box-Based Segment Anything Model for Large-Scale Medical Image Datasets

https://github.com/russelljeffrey/FedBrain-Distill

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FedBrain-Distill: Communication-Efficient Federated Brain Tumor Classification Using Ensemble Knowledge Distillation on Non-IID Data

https://github.com/rusu4943/3d-sfuda

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Cross-Domain Distribution Alignment for Segmentation of Private Unannotated 3D Medical Images

https://github.com/ruveydayilmaz0/bvdm

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Annotated Biomedical Video Generation using Denoising Diffusion Probabilistic Models and Flow Fields

https://github.com/ruveydayilmaz0/cascaded_diffusion

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Cascaded Diffusion Models for 2D and 3D Microscopy Image Synthesis to Enhance Cell Segmentation

https://github.com/rvignav/CT2Xray

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COVID-19 Lung Lesion Segmentation Using a Sparsely Supervised Mask R-CNN on Chest X-rays Automatically Computed from Volumetric CTs

https://github.com/saadwazir/HistoSeg

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HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images

https://github.com/saadwazir/ReN-UNet

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Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature Fusion

https://github.com/saadwazir/mcads-decoder

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Rethinking Decoder Design: Improving Biomarker Segmentation Using Depth-to-Space Restoration and Residual Linear Attention

https://github.com/sacmehta/YNet

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Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images

https://github.com/sakurajimamaiii/protokd

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Prototype Knowledge Distillation for Medical Segmentation with Missing Modality

https://github.com/sakurajimamaiii/tsd

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Feature Alignment and Uniformity for Test Time Adaptation

https://github.com/samra-irshad/3d-boundary-constrained-networks

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Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks

https://github.com/samxmot/RANDGAN

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RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray

https://github.com/saniaesk/skin-lesion-segmentation

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Skin Lesion Segmentation Improved by Transformer-based Networks with Inter-scale Dependency Modeling

https://github.com/sarihust/sammed

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Leveraging SAM for Single-Source Domain Generalization in Medical Image Segmentation

https://github.com/scale-lab/FCNiris

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A Resource-Efficient Embedded Iris Recognition System Using Fully Convolutional Networks

https://github.com/scan-nrad/e3nn_unet

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Learning from spatially inhomogenous data: resolution-adaptive convolutions for multiple sclerosis lesion segmentation

https://github.com/schroeterjulien/midl-2022-segmentation-consistent-lesion-counting

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Segmentation-Consistent Probabilistic Lesion Counting

https://github.com/scott-yjyang/hcdg

공식 코드

HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation

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