Papers with Code 아카이브
Computed Tomography (CT)
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The term “computed tomography”, or CT, refers to a computerized x-ray imaging procedure in which a narrow beam of x-rays is aimed at a patient and quickly rotated around the body, producing signals that are processed by the machine's computer to generate cross-sectional images—or “slices”—of the body.
<span style="color:grey; opacity: 0.6">( Image credit: Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/leeh43/Singularity_Deeplesion
https://github.com/ljvmiranda921/pyswarms
https://github.com/maftouni/Corona_CT_Classification
https://github.com/manuelhz/dissertation
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
https://github.com/mareikethies/geometry_gradients_ct
Gradient-Based Geometry Learning for Fan-Beam CT Reconstruction
https://github.com/markson14/WheatDet
https://github.com/martinzlocha/anchor-optimization
Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels
https://github.com/med-air/Contrastive-COVIDNet
https://github.com/michal-crosta/GIT19
https://github.com/mlf-core/liver-ct-segmentation
https://github.com/monikagrewal/End2EndLandmarks
An End-to-end Deep Learning Approach for Landmark Detection and Matching in Medical Images
https://github.com/mrgiovanni/dissertation
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
https://github.com/mundher/local-global
https://github.com/narges-sa/COVID-CT-Segmentation
Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images
https://github.com/neuro-ml/straighten
Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification
https://github.com/noamelata/AdaSense
Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling
https://github.com/oubino/General-location-finding
https://github.com/pgdelbosque/GAN-MRI
https://github.com/qiuliwang/lidc-idri-toolbox-python
https://github.com/raun1/Diagnostic-Classification-Of-Lung-Nodules-Using-3D-Neural-Networks
Diagnostic Classification Of Lung Nodules Using 3D Neural Networks
https://github.com/raun1/ISBI2018-Diagnostic-Classification-Of-Lung-Nodules-Using-3D-Neural-Networks
Diagnostic Classification Of Lung Nodules Using 3D Neural Networks
https://github.com/reyvaz/pneumothorax_detection
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
https://github.com/reyvaz/steel-defect-segmentation
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
https://github.com/riadhassan/UDBA-UNet
Uncertainty Driven Bottleneck Attention U-net for Organ at Risk Segmentation
https://github.com/rizalmaulanaa/robustness_of_prob_u_net
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
https://github.com/ruanyuhui/adn-qsdl
ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
https://github.com/ryota2425/maskrcnn-benchmark
https://github.com/sabapathyn/Master
https://github.com/sauravmishra1710/UNet-Plus-Plus---Brain-Tumor-Segmentation
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
https://github.com/selim-iitdu/STANCT
STAN-CT: Standardizing CT Image using Generative Adversarial Network
https://github.com/shujams/CT-AI-Deep-Learning-Project
https://github.com/stanfordmimi/merlin
Merlin: A Vision Language Foundation Model for 3D Computed Tomography
https://github.com/sulam-group/learned-proximal-networks
End-to-end reconstruction meets data-driven regularization for inverse problems
https://github.com/suryachintu/RSNA-Intracranial-Hemorrhage-Detection
Practical Window Setting Optimization for Medical Image Deep Learning
https://github.com/talhaanwarch/Corona_Virus/tree/master/CT_scan
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.