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Medical Image Analysis

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/jongdory/damt

공식 코드

Domain Aware Multi-Task Pretraining of 3D Swin Transformer for T1-weighted Brain MRI

https://github.com/joshuachou2018/pppml-hmi

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Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI

https://github.com/julschoen/latent-space-exploration-ct

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Interpreting Latent Spaces of Generative Models for Medical Images using Unsupervised Methods

https://github.com/juntaojianggavin/rwkv-unet

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RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation

https://github.com/junyuchen245/TransMorph_Transformer_for_Medical_Image_Registration

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TransMorph: Transformer for unsupervised medical image registration

https://github.com/karryren/uml

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Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation

https://github.com/kasraborazjani/dgb-pcw-fl

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Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities

https://github.com/kate-sann5100/crossinstitutionfewshotsegmentation

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Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration

https://github.com/kaz-k/sketch-based-medical-image-retrieval

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Sketch-based Medical Image Retrieval

https://github.com/key1589745/decouple_predict

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Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation

https://github.com/kiananvari/mtms-a-domain-adaptive-meta-knowledge-distillation-model-for-medical-image-classification

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Multiple Teachers-Meticulous Student: A Domain Adaptive Meta-Knowledge Distillation Model for Medical Image Classification

https://github.com/kibrom2b/LFB-Net

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Learning With Context Feedback Loop for Robust Medical Image Segmentation

https://github.com/kidswithtokens/medsegdiff

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MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer

https://github.com/kli-lalala/Dual-Teacher-

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Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentation

https://github.com/krishnabits001/domain_specific_cl

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Contrastive learning of global and local features for medical image segmentation with limited annotations

https://github.com/lachinov/brats2019

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Knowledge Distillation for Brain Tumor Segmentation

https://github.com/larissafrodrigues/sickle-cell-classification-ENIAC2023

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Improving Sickle Cell Disease Classification: A Fusion of Conventional Classifiers, Segmented Images, and Convolutional Neural Networks

https://github.com/learningtitans/cvpr-skin-solo-ensemble

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Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification

https://github.com/leixiaofeng-astar/omia9-zian

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Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks

https://github.com/levayz/dinov2-based-self-supervised-learning

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DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentation

https://github.com/lhaof/cgt

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Cell Graph Transformer for Nuclei Classification

https://github.com/lhaof/nnmamba

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nnMamba: 3D Biomedical Image Segmentation, Classification and Landmark Detection with State Space Model

https://github.com/lichen14/tower

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A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Biomedical Microscopy Images

https://github.com/lighterswang/awesome-active-learning-for-medical-image-analysis

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A comprehensive survey on deep active learning in medical image analysis

https://github.com/lisatwyw/cov19

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Severity classification of ground-glass opacity via 2-D convolutional neural network and lung CT scans: a 3-day exploration

https://github.com/lswzjuer/NAS-WDAN

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MixSearch: Searching for Domain Generalized Medical Image Segmentation Architectures

https://github.com/lucasmansilla/ACRN_Chest_X-ray_IA

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Learning Deformable Registration of Medical Images with Anatomical Constraints

https://github.com/lucasmansilla/NIH_chest_xray14_segmentations

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Learning Deformable Registration of Medical Images with Anatomical Constraints

https://github.com/luffy03/large-scale-medical

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Large-Scale 3D Medical Image Pre-training with Geometric Context Priors

https://github.com/luffy03/voco

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VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for 3D Medical Image Analysis

https://github.com/luukboulogne/stoic2021-baseline

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The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data

https://github.com/lx6c78/vision-mamba-a-comprehensive-survey-and-taxonomy

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Vision Mamba: A Comprehensive Survey and Taxonomy

https://github.com/madhavaprasath23/awesome-mamba-papers-on-medical-domain

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A Comprehensive Survey of Mamba Architectures for Medical Image Analysis: Classification, Segmentation, Restoration and Beyond

https://github.com/mahmoodlab/uni

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Evaluating Pre-trained Convolutional Neural Networks and Foundation Models as Feature Extractors for Content-based Medical Image Retrieval

https://github.com/mahsa91/IntelligentMasking-MLMI2022

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Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis

https://github.com/marinbenc/ssl-for-medical-object-detection

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Self-Supervised Learning as a Means To Reduce the Need for Labeled Data in Medical Image Analysis

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