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://bitbucket.org/JianboJiao/ssus2mri

공식 코드

Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis

https://bitbucket.org/raghavian/lotenet_pytorch

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Tensor Networks for Medical Image Classification

https://github.com/13204942/ft_methods_for_fetal_head_segmentation

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Evaluate Fine-tuning Strategies for Fetal Head Ultrasound Image Segmentation with U-Net

https://github.com/983632847/USCL

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USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning

https://github.com/Algolzw/SPAC-Deformable-Registration

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Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration

https://github.com/Alison-brie/MultiPropReg

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Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond

https://github.com/AnandK27/GS-TransUNet

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GS-TransUNet: Integrated 2D Gaussian Splatting and Transformer UNet for Accurate Skin Lesion Analysis

https://github.com/AngeLouCN/CFPNet-Medicine

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CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation

https://github.com/BPYap/Cut-Paste-Consistency

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Cut-Paste Consistency Learning for Semi-Supervised Lesion Segmentation

https://github.com/BanafshehKarimian/ModalityPairing

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CLIP-IT: CLIP-based Pairing for Histology Images Classification

https://github.com/BioIntelligence-Lab/openjphpy

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Towards Resource-Efficient Streaming of Large-Scale Medical Image Datasets for Deep Learning

https://github.com/CapsuleEndoscope/VirtualCapsuleEndoscopy

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VR-Caps: A Virtual Environment for Capsule Endoscopy

https://github.com/ChenLiu-1996/ImageFlowNet

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ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images

https://github.com/DIAL-RPI/Task-Oriented-CT-Denoising_TOD-Net

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Task-Oriented Low-Dose CT Image Denoising

https://github.com/Daniyanaj/vMixer

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Improving 3D Medical Image Segmentation at Boundary Regions using Local Self-attention and Global Volume Mixing

https://github.com/Deep-Imaging-Group/RED-WGAN

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Denoising of 3-D Magnetic Resonance Images Using a Residual Encoder-Decoder Wasserstein Generative Adversarial Network

https://github.com/DinhHieuHoang/SAM3D

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SAM3D: Segment Anything Model in Volumetric Medical Images

https://github.com/Dylan-H-Wang/msf-wsi

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A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images

https://github.com/ECNUACRush/H3DE-Net

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H3DE-Net: Efficient and Accurate 3D Landmark Detection in Medical Imaging

https://github.com/ETVP/medical-image-list

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A Systematic Collection of Medical Image Datasets for Deep Learning

https://github.com/FBLADL/ACPL

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ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification

https://github.com/GabrieleLozupone/LDAE

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Latent Diffusion Autoencoders: Toward Efficient and Meaningful Unsupervised Representation Learning in Medical Imaging

https://github.com/Gerda92/adversarial_transfer_factors

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Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors

https://github.com/GirinChutia/Misahub-Capsule-Vision-2024-Solution-eAI

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Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy

https://github.com/Guzaiwang/CE-Net

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CE-Net: Context Encoder Network for 2D Medical Image Segmentation

https://github.com/HealthX-Lab/HGI-SAM

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Weakly Supervised Intracranial Hemorrhage Segmentation using Head-Wise Gradient-Infused Self-Attention Maps from a Swin Transformer in Categorical Learning

https://github.com/HealthX-Lab/MedCLIP-SAM

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MedCLIP-SAM: Bridging Text and Image Towards Universal Medical Image Segmentation

https://github.com/HuCaoFighting/Swin-Unet

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Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation

https://github.com/HuaiChen-1994/LDLearning

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Unsupervised Learning of Local Discriminative Representation for Medical Images

https://github.com/ICT-MIRACLE-lab/CTSpine1K

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CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography

https://github.com/IMSY-DKFZ/self-distilled-swin

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Self-distillation for surgical action recognition

https://github.com/IMSY-DKFZ/task-fingerprinting

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Beyond Knowledge Silos: Task Fingerprinting for Democratization of Medical Imaging AI

https://github.com/JLiangLab/TransVW

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Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learning

https://github.com/JasonZuu/SegX

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SegX: Improving Interpretability of Clinical Image Diagnosis with Segmentation-based Enhancement

https://github.com/Jeongseungwoo/Efficient-Continuous-Manifold-Learning

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Deep Efficient Continuous Manifold Learning for Time Series Modeling

https://github.com/JerrryNie/ConceptCLIP

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ConceptCLIP: Towards Trustworthy Medical AI via Concept-Enhanced Contrastive Langauge-Image Pre-training

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