Papers with Code 아카이브

Medical Image Analysis

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전체 기법전체 데이터셋

하위 태스크

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보존된 평가 기록

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코드

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

https://github.com/JimmyCai91/DeepLesionAnnotation

공식 코드

Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale

https://github.com/JinYinzhu/featurecollapse

공식 코드

Measuring Feature Dependency of Neural Networks by Collapsing Feature Dimensions in the Data Manifold

https://github.com/KSH0660/BrainTumor

공식 코드

Synthesis of Brain Tumor MR Images for Learning Data Augmentation

https://github.com/LIVIAETS/surface-loss

공식 코드

Boundary loss for highly unbalanced segmentation

https://github.com/LOUEY233/Causal-representation-in-vision

공식 코드

A Survey on Causal Representation Learning and Future Work for Medical Image Analysis

https://github.com/Lauraxy/Multi_Modal_Registration

공식 코드

Deform-GAN:An Unsupervised Learning Model for Deformable Registration

https://github.com/LiheYoung/UniMatch

공식 코드

Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation

https://github.com/M3DV/AlignShift

공식 코드

Asymmetric 3D Context Fusion for Universal Lesion Detection

https://github.com/MIC-DKFZ/batchgenerators

공식 코드

Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection

https://github.com/MIMBCD-UI/prototype-multi-modality

공식 코드

BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

https://github.com/MMIV-ML/DLMI2018

공식 코드

An overview of deep learning in medical imaging focusing on MRI

https://github.com/Manoj-Sh-AI/Diabetic-Retinopathy-Detection-and-Clasification-System

공식 코드

Detection and Classification of Diabetic Retinopathy using Deep Learning Algorithms for Segmentation to Facilitate Referral Recommendation for Test and Treatment Prediction

https://github.com/MedMNIST/MedMNIST

공식 코드

MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis

https://github.com/MingR-Ma/SymTrans

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Symmetric Transformer-based Network for Unsupervised Image Registration

https://github.com/MrGiovanni/ModelsGenesis

공식 코드

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

https://github.com/NifTK/NiftyNet

공식 코드

NiftyNet: a deep-learning platform for medical imaging

https://github.com/OpenGVLab/SAM-Med2D

공식 코드

SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

https://github.com/Project-MONAI/research-contributions/tree/master/SwinUNETR

공식 코드

Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis

https://github.com/Project-MONAI/research-contributions/tree/master/SwinUNETR/BRATS21

공식 코드

Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

https://github.com/Qianyu1226/Crosslink-Net

공식 코드

Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions

https://github.com/RL4M/PCRLv2

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PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis

https://github.com/Raman1121/FairTune

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FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis

https://github.com/Raman1121/diffusion_memorization

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The Devil is in the Prompts: De-Identification Traces Enhance Memorization Risks in Synthetic Chest X-Ray Generation

https://github.com/RicoLeehdu/SISeg

공식 코드

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine

https://github.com/RunkaiZhao/PointNeuron

공식 코드

PointNeuron: 3D Neuron Reconstruction via Geometry and Topology Learning of Point Clouds

https://github.com/Ryann-Ran/Lad

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Devil is in Details: Locality-Aware 3D Abdominal CT Volume Generation for Self-Supervised Organ Segmentation

https://github.com/SarderLab/H-AI-L

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Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis

https://github.com/SarielMa/Increasing-Margin-Adversarial-Training

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Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks

https://github.com/Sean9511/Additive-Tensor-Decomposition-Considering-Structural-Data-Information

공식 코드

Additive Tensor Decomposition Considering Structural Data Information

https://github.com/Seolen/weak_seg_via_shape_model

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Weakly Supervised Volumetric Segmentation via Self-taught Shape Denoising Model

https://github.com/Shrajan/IB_U_Nets

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IB-U-Nets: Improving medical image segmentation tasks with 3D Inductive Biased kernels

https://github.com/StefanDenn3r/Unsupervised_Anomaly_Detection_Brain_MRI

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Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study

https://github.com/Tencent/MedicalNet

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Med3D: Transfer Learning for 3D Medical Image Analysis

https://github.com/TheoEst/abdominal_registration

공식 코드

Deep learning based registration using spatial gradients and noisy segmentation labels

https://github.com/TheoEst/hippocampus_registration

공식 코드

Deep learning based registration using spatial gradients and noisy segmentation labels

https://github.com/ToFec/OneShotImageRegistration

공식 코드

One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking

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