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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/mindflow-institue/transception

공식 코드

Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach

https://github.com/mindflow-institue/waveformer

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Unlocking Fine-Grained Details with Wavelet-based High-Frequency Enhancement in Transformers

https://github.com/minimel/stochasticbatchal

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Active learning for medical image segmentation with stochastic batches

https://github.com/mirthai/csa-net

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A Flexible 2.5D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention

https://github.com/mist-medical/MIST

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MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Framework

https://github.com/mj129/cipa

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Cross-Modal Interactive Perception Network with Mamba for Lung Tumor Segmentation in PET-CT Images

https://github.com/mjahanifar/mDRFI_matlab

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Supervised Saliency Map Driven Segmentation of the Lesions in Dermoscopic Images

https://github.com/mkang315/mctseg

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A Multimodal Feature Distillation with CNN-Transformer Network for Brain Tumor Segmentation with Incomplete Modalities

https://github.com/ml-ailab/ophglm

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OphGLM: Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and Dialogue

https://github.com/mlmed/torchxrayvision

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TorchXRayVision: A library of chest X-ray datasets and models

https://github.com/mlyg/generalised-focal-loss

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Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation

https://github.com/mlyg/unified-focal-loss

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Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation

https://github.com/mmu-dermatology-research/multimodal-grf

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Gaussian Random Fields as an Abstract Representation of Patient Metadata for Multimodal Medical Image Segmentation

https://github.com/mobarakol/3D_Attention_UNet

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Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet

https://github.com/mobarakol/p-cd

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Paced-Curriculum Distillation with Prediction and Label Uncertainty for Image Segmentation

https://github.com/modaresimr/adaptive_mis

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Boosting Medical Image Segmentation Performance with Adaptive Convolution Layer

https://github.com/mohofar/CovidCtNet

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CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image

https://github.com/monsoon49/mslau-net

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MSLAU-Net: A Hybird CNN-Transformer Network for Medical Image Segmentation

https://github.com/mostafa-ghazi/fast-aid-brain

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FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain

https://github.com/mostafa-ghazi/mri-augmentation

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FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain

https://github.com/moucheng2017/Learn_Noisy_Labels_Medical_Images

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Disentangling Human Error from the Ground Truth in Segmentation of Medical Images

https://github.com/moucheng2017/Pay_Attention_To_Mistakes

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Learning To Pay Attention To Mistakes

https://github.com/moucheng2017/emssl

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Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi-Supervised Segmentation

https://github.com/moucheng2017/mismatchssl

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MisMatch: Calibrated Segmentation via Consistency on Differential Morphological Feature Perturbations with Limited Labels

https://github.com/mqinghe/midss

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Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation

https://github.com/mqinghe/synfoc

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Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation

https://github.com/mqinghe/ust-run

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Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation

https://github.com/mrblankness/lightm-unet

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LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation

https://github.com/mrkolarik/transfer2d3d

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Planar 3D Transfer Learning for End to End Unimodal MRI Unbalanced Data Segmentation

https://github.com/mudit-adityaja/unsegmedgat

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UnSegMedGAT: Unsupervised Medical Image Segmentation using Graph Attention Networks Clustering

https://github.com/muhamadusman/assist

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Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models

https://github.com/multimodallearning/dg-tta

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DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time Adaptation

https://github.com/multimodallearning/hierarchical-dense-ssl

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Self-supervised Learning of Dense Hierarchical Representations for Medical Image Segmentation

https://github.com/multimodallearning/samcarriestheburden

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SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation

https://github.com/naamiinepal/medvlsm

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Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models

https://github.com/naamiinepal/vlsm-adapter

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VLSM-Adapter: Finetuning Vision-Language Segmentation Efficiently with Lightweight Blocks

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