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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/ddrrnn123/casc-ai

공식 코드

CASC-AI: Consensus-aware Self-corrective AI Agents for Noise Cell Segmentation

https://github.com/debeshjha/pvtformer

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CT Liver Segmentation via PVT-based Encoding and Refined Decoding

https://github.com/deep-mi/neonatevinna

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VINNA for Neonates -- Orientation Independence through Latent Augmentations

https://github.com/deepmed-lab-ecnu/bcp

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Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation

https://github.com/deepmed-lab-ecnu/fs_medsam2

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RevSAM2: Prompt SAM2 for Medical Image Segmentation via Reverse-Propagation without Fine-tuning

https://github.com/deepmed-lab-ecnu/magicnet

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MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery

https://github.com/deeptag/cstm

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Continuous Spatio-Temporal Memory Networks for 4D Cardiac Cine MRI Segmentation

https://github.com/delugewalker/CGA-U-Net

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Category Guided Attention Network for Brain Tumor Segmentation in MRI

https://github.com/devavrattomar/sst

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Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation

https://github.com/deweihu/adaptdiff

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AdaptDiff: Cross-Modality Domain Adaptation via Weak Conditional Semantic Diffusion for Retinal Vessel Segmentation

https://github.com/dewenzeng/cl-tci

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Contrastive Learning with Temporal Correlated Medical Images: A Case Study using Lung Segmentation in Chest X-Rays

https://github.com/dewenzeng/positional_cl

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Positional Contrastive Learning for Volumetric Medical Image Segmentation

https://github.com/diagnijmegen/prostatemr-ussl

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Uncertainty-Aware Semi-Supervised Learning for Prostate MRI Zonal Segmentation

https://github.com/dial-rpi/fedcross

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Federated Cross Learning for Medical Image Segmentation

https://github.com/dinhhieuhoang/dam-ca-infantbrain

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DAM-AL: Dilated Attention Mechanism with Attention Loss for 3D Infant Brain Image Segmentation

https://github.com/dinhhieuhoang/meganet

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MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation

https://github.com/dipaco/mole-classification

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Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging

https://github.com/divelab/Non-local-U-Nets

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Non-local U-Net for Biomedical Image Segmentation

https://github.com/dliu5812/PFFNet

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Panoptic Feature Fusion Net: A Novel Instance Segmentation Paradigm for Biomedical and Biological Images

https://github.com/dogabasaran/lesion-synthesis

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Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images

https://github.com/dogabasaran/lesionmix

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LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation

https://github.com/dong845/mcp-medsam

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MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day

https://github.com/dootmaan/mt-unet

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Mixed Transformer U-Net For Medical Image Segmentation

https://github.com/dootmaan/pfseg-full

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Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance

https://github.com/dorltcheng/Transfer-Learning-U-Net-Deep-Learning-for-Lung-Ultrasound-Segmentation

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Transfer Learning U-Net Deep Learning for Lung Ultrasound Segmentation

https://github.com/dpbenalcazar/ArtificialDilation

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Artificial Pupil Dilation for Data Augmentation in Iris Semantic Segmentation

https://github.com/dragondescentzerotsu/ssm-sam

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Self-Sampling Meta SAM: Enhancing Few-shot Medical Image Segmentation with Meta-Learning

https://github.com/dsaragih/diffuse-gen

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Using Diffusion Models to Generate Synthetic Labelled Data for Medical Image Segmentation

https://github.com/ducnt9907/ColonFormer

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ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentation

https://github.com/dumyCq/CFPNet

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ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video

https://github.com/duong-db/u-lite

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1M parameters are enough? A lightweight CNN-based model for medical image segmentation

https://github.com/duttapallabi2907/u-vixlstm

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Are Vision xLSTM Embedded UNet More Reliable in Medical 3D Image Segmentation?

https://github.com/duttapallabi2907/uvixlstm

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Are Vision xLSTM Embedded UNet More Reliable in Medical 3D Image Segmentation?

https://github.com/duyhominhnguyen/LVM-Med

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LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching

https://github.com/dyh127/Modeling-the-Probabilistic-Distribution-of-Unlabeled-Data

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Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentation

https://github.com/dyh127/s2vnet

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Clustering Propagation for Universal Medical Image Segmentation

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