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/matt3o/autopet2-submission

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AutoPET Challenge 2023: Sliding Window-based Optimization of U-Net

https://github.com/matteo-ronchetti/torch-radon

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TorchRadon: Fast Differentiable Routines for Computed Tomography

https://github.com/matthewachan/hyperdm

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Estimating Epistemic and Aleatoric Uncertainty with a Single Model

https://github.com/matthewyccheung/conformal-metric

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Metric-Guided Conformal Bounds for Probabilistic Image Reconstruction

https://github.com/maxtschuchnig/enhancingsyntheticctfromcbctviamultimodalfusionandend-to-endregistration

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Enhancing Synthetic CT from CBCT via Multimodal Fusion and End-To-End Registration

https://github.com/maybe198376/LEARN-Plus-Plus

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LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT

https://github.com/mazurowski-lab/SlicerSegmentHumanBody

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Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2

https://github.com/mazurowski-lab/segment-anything-medical-evaluation

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Segment anything model 2: an application to 2D and 3D medical images

https://github.com/mazurowski-lab/slm-sam2

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Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2

https://github.com/mbkiss/2detectcodes

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2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning

https://github.com/medcai/l2snet

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A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT Images

https://github.com/mhabijan/medical_images_generation

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Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial Networks

https://github.com/mic-dkfz/bodypartregression

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Body Part Regression for CT Images

https://github.com/mic-dkfz/particleseg3d

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ParticleSeg3D: A Scalable Out-of-the-Box Deep Learning Segmentation Solution for Individual Particle Characterization from Micro CT Images in Mineral Processing and Recycling

https://github.com/miclab-unicamp/medpseg

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MEDPSeg: Hierarchical polymorphic multitask learning for the segmentation of ground-glass opacities, consolidation, and pulmonary structures on computed tomography

https://github.com/ming053l/e2d

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A Closer Look at Spatial-Slice Features Learning for COVID-19 Detection

https://github.com/miracle-center/ctspine1k

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

https://github.com/mirthAI/CIS-UNet

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CIS-UNet: Multi-Class Segmentation of the Aorta in Computed Tomography Angiography via Context-Aware Shifted Window Self-Attention

https://github.com/mmlowes/inr_reg_lvmyo

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Implicit Neural Representations for Registration of Left Ventricle Myocardium During a Cardiac Cycle

https://github.com/mohofar/CovidCtNet

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CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image

https://github.com/moloud1987/UncertaintyFuseNet-for-COVID-19-Classification

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UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection

https://github.com/mrgiovanni/difftumor

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Towards Generalizable Tumor Synthesis

https://github.com/mrgiovanni/pixel2cancer

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From Pixel to Cancer: Cellular Automata in Computed Tomography

https://github.com/mrgiovanni/suprem

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How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?

https://github.com/mughanibu/DeepMAR

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Fast Enhanced CT Metal Artifact Reduction using Data Domain Deep Learning

https://github.com/mungomeng/deepmts

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DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT

https://github.com/mungomeng/survival-deepmts

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DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT

https://github.com/mzeegers/ADJUST

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ADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography

https://github.com/mzeegers/DeepFODDataGenerator

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A tomographic workflow to enable deep learning for X-ray based foreign object detection

https://github.com/nadeemlab/dosertx

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Domain Knowledge Driven 3D Dose Prediction Using Moment-Based Loss Function

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

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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/neheller/kits19

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The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

https://github.com/neuro-ml/anchor-free-genant

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Interpretable Vertebral Fracture Quantification via Anchor-Free Landmarks Localization

https://github.com/neuro-ml/vertebral-fractures-severity

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Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification

https://github.com/niuchuangnn/noise2sim

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Suppression of Correlated Noise with Similarity-based Unsupervised Deep Learning

https://github.com/noacahan/x-ray2ctpa

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X-ray2CTPA: Generating 3D CTPA scans from 2D X-ray conditioning

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