Papers with Code 아카이브

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/mohammedsb/dinov2formedical

공식 코드

Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks

https://github.com/mohammedsb/dinov2forradiology

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Evaluating General Purpose Vision Foundation Models for Medical Image Analysis: An Experimental Study of DINOv2 on Radiology Benchmarks

https://github.com/mohofar/beta-rank

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Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis

https://github.com/mominul-ssv/cossif

공식 코드

CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets

https://github.com/msiip/medm-vl

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MedM-VL: What Makes a Good Medical LVLM?

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

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UnSegMedGAT: Unsupervised Medical Image Segmentation using Graph Attention Networks Clustering

https://github.com/mueller-franzes/mst

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Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2

https://github.com/multimodallearning/convexadam

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Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021

https://github.com/multimodallearning/deep-geo-reg

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Deep learning based geometric registration for medical images: How accurate can we get without visual features?

https://github.com/multimodallearning/pdd_net

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Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networks

https://github.com/mungomeng/registration-autofuse

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AutoFuse: Automatic Fusion Networks for Deformable Medical Image Registration

https://github.com/mungomeng/registration-corrmlp

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Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration

https://github.com/mungomeng/registration-nice-net

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Non-iterative Coarse-to-fine Registration based on Single-pass Deep Cumulative Learning

https://github.com/mungomeng/registration-nice-trans

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Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration

https://github.com/nainye/radiomicsretrieval

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RadiomicsRetrieval: A Customizable Framework for Medical Image Retrieval Using Radiomics Features

https://github.com/ncbi-nlp/COVID-19-CT-CXR

공식 코드

COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature

https://github.com/negralessio/thesis-visual-in-context-learning

공식 코드

Conquering the Retina: Bringing Visual in-Context Learning to OCT

https://github.com/nhussein/promptsmooth

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PromptSmooth: Certifying Robustness of Medical Vision-Language Models via Prompt Learning

https://github.com/ninatu/anomaly_detection

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Anomaly Detection in Medical Imaging with Deep Perceptual Autoencoders

https://github.com/nkuhealong/dvpt

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DVPT: Dynamic Visual Prompt Tuning of Large Pre-trained Models for Medical Image Analysis

https://github.com/noureddinekhiati/semi-supervised-lung-segmentation

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Shape-aware synthesis of pathological lung CT scans using CycleGAN for enhanced semi-supervised lung segmentation

https://github.com/nubagcilab/icassp2025-fdgpolyp

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Frequency-Based Federated Domain Generalization for Polyp Segmentation

https://github.com/nus-tim/medaugment

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MedAugment: Universal Automatic Data Augmentation Plug-in for Medical Image Analysis

https://github.com/nyukat/GMIC

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An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization

https://github.com/nyukat/mammography_metarepository

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Meta-repository of screening mammography classifiers

https://github.com/obiyoag/evi-cem

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Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis

https://github.com/olivesgatech/olives_dataset

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OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics

https://github.com/omfsdigital/cyto-r-cnn

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Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images

https://github.com/onofreylab/sre_conv_segm_isbi2025

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Improved Vessel Segmentation with Symmetric Rotation-Equivariant U-Net

https://github.com/openmedlab/MedFM

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MedFMC: A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification

https://github.com/openmedlab/medlsam

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MedLSAM: Localize and Segment Anything Model for 3D CT Images

https://github.com/osupcvlab/mobileunetr

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MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation

https://github.com/ovo1111/efficientsubclasslearning

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Efficient Subclass Segmentation in Medical Images

https://github.com/ozanciga/self-supervised-histopathology

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Self supervised contrastive learning for digital histopathology

https://github.com/paperunderreviewdeeplearning/optimization

공식 코드

When do Convolutional Neural Networks Stop Learning?

https://github.com/parastoosotoudeh/pathowave

공식 코드

PathoWAve: A Deep Learning-based Weight Averaging Method for Improving Domain Generalization in Histopathology Images

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