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Anatomy
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://git5.cs.fau.de/folle/sr-cest
공식 코드Towards Super-Resolution CEST MRI for Visualization of Small Structures
https://github.com/AyushiSinha/AutoInitialization
공식 코드Towards automatic initialization of registration algorithms using simulated endoscopy images
https://github.com/BBillot/hypothalamus_seg
공식 코드Ultra-high resolution multimodal MRI densely labelled holistic structural brain atlas
https://github.com/CAiM-lab/PPO
공식 코드Reinforcement Learning of Musculoskeletal Control from Functional Simulations
https://github.com/Cerenaut/bilateral-brain
공식 코드Deep learning in a bilateral brain with hemispheric specialization
https://github.com/ClancyZhou/P_Net_Anomaly_Detection
공식 코드Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images
https://github.com/DIAGNijmegen/prostateMR_3D-CAD-csPCa
공식 코드https://github.com/Dandy5721/ICML2024
공식 코드https://github.com/DarkoBomer/VCA-Net
공식 코드Stroke Lesion Segmentation with Visual Cortex Anatomy Alike Neural Nets
https://github.com/FJDorfner/Anatomy-Aware-Classification-axSpA
공식 코드https://github.com/FabianSchubert/frontiers_dendritic_coincidence_detection
공식 코드Nonlinear Dendritic Coincidence Detection for Supervised Learning
https://github.com/HEPIUSLAB/ultrasound_spinal_cord_dataset
공식 코드https://github.com/HieuPhan33/MaskGAN
공식 코드Structure-Preserving Synthesis: MaskGAN for Unpaired MR-CT Translation
https://github.com/ICT-MIRACLE-lab/YOLO_Universal_Anatomical_Landmark_Detection
공식 코드You Only Learn Once: Universal Anatomical Landmark Detection
https://github.com/IDU-CVLab/COV19D
공식 코드Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images
https://github.com/IPMI-ICNS-UKE/NFSegmentationPipeline
공식 코드https://github.com/JLiangLab/SemanticGenesis
공식 코드https://github.com/JLiangLab/TransVW
공식 코드https://github.com/JoHof/lungmask
공식 코드https://github.com/JohnSnowLabs/spark-nlp-workshop/tree/master/tutorials/Certification_Trainings/Healthcare
공식 코드Improving Clinical Document Understanding on COVID-19 Research with Spark NLP
https://github.com/LeapLabTHU/EchoWorld
공식 코드EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidance
https://github.com/LongchaoDa/SegmentAsYouWish
공식 코드Segment as You Wish -- Free-Form Language-Based Segmentation for Medical Images
https://github.com/LourentzouTBD/ChestImaGenomeChangeDetection
공식 코드https://github.com/LucasFidon/HardnessWeightedSampler
공식 코드Distributionally Robust Segmentation of Abnormal Fetal Brain 3D MRI
https://github.com/MECLabTUDA/VoxelSceneGraph
공식 코드https://github.com/MIC-DKFZ/batchgenerators
공식 코드Anatomy-informed Data Augmentation for Enhanced Prostate Cancer Detection
https://github.com/MathFLDS/HUMUS-Net
공식 코드HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction
https://github.com/MengyunQ/AgeHeart
공식 코드Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data
https://github.com/MrGiovanni/ModelsGenesis
공식 코드Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
https://github.com/MungoMeng/Image-Registration-AAN
공식 코드Enhancing Medical Image Registration via Appearance Adjustment Networks
https://github.com/NIC-VICOROB/SUNet-architecture
공식 코드Acute and sub-acute stroke lesion segmentation from multimodal MRI
https://github.com/NIC-VICOROB/stroke-mri-segmentation
공식 코드Acute and sub-acute stroke lesion segmentation from multimodal MRI
https://github.com/NeuronXJTU/CRBNet
공식 코드Ultrasound Nodule Segmentation Using Asymmetric Learning with Simple Clinical Annotation
https://github.com/P-Song/CDLMRI
공식 코드Coupled Dictionary Learning for Multi-contrast MRI Reconstruction
https://github.com/Project-MONAI/research-contributions/tree/master/SwinUNETR
공식 코드Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.