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/workingcoder/EDCNN

공식 코드

EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising

https://github.com/xeschong/stacom2024

공식 코드

Improved 3D Whole Heart Geometry from Sparse CMR Slices

https://github.com/xiaoqi-zhao-dlut/msnet

공식 코드

M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation

https://github.com/xingorno/deepregs2v

공식 코드

Deep Regression 2D-3D Ultrasound Registration for Liver Motion Correction in Focal Tumor Thermal Ablation

https://github.com/xinli0928/COVID-Xray

공식 코드

COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays

https://github.com/xiongchaochen/dudocfnet-multitask

공식 코드

Dual-Domain Coarse-to-Fine Progressive Estimation Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT

https://github.com/xiongchaochen/dusfe_crossregistration

공식 코드

Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT

https://github.com/yang-song/score_inverse_problems

공식 코드

Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

https://github.com/yasminzhang/adcr

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Learning Difference-of-Convex Regularizers for Inverse Problems: A Flexible Framework with Theoretical Guarantees

https://github.com/ycl92/tmd-ldct

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Cross-domain Denoising for Low-dose Multi-frame Spiral Computed Tomography

https://github.com/yejin0111/autopet2022_blackbean

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Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT Scans

https://github.com/yhsong98/ct-us-i2i-translation

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S-CycleGAN: Semantic Segmentation Enhanced CT-Ultrasound Image-to-Image Translation for Robotic Ultrasonography

https://github.com/ymli39/DeepSEED-3D-ConvNets-for-Pulmonary-Nodule-Detection

공식 코드

DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection

https://github.com/yqx7150/gmsd

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Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction

https://github.com/yqx7150/osdm

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One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging

https://github.com/yransun/difr3ct

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DIFR3CT: Latent Diffusion for Probabilistic 3D CT Reconstruction from Few Planar X-Rays

https://github.com/zhaozsq/airway_segmentation

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Two-stage Contextual Transformer-based Convolutional Neural Network for Airway Extraction from CT Images

https://github.com/zhi-xuan-chen/reg2rg

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Large Language Model with Region-guided Referring and Grounding for CT Report Generation

https://github.com/zhibaishouheilab/HealthiVert-GAN

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HealthiVert-GAN: A Novel Framework of Pseudo-Healthy Vertebral Image Synthesis for Interpretable Compression Fracture Grading

https://github.com/zhouyupop/fracseg-net

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Towards Cross-Scale Attention and Surface Supervision for Fractured Bone Segmentation in CT

https://github.com/zi-yuanyang/hyperfed

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Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging

https://github.com/zjk1988/DAN-Net

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DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction

https://gitlab.com/UM-CDS/low-dose-ct-denoising

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Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation Study

https://gitlab.oit.duke.edu/cvit-public/cvit_revicovid19

공식 코드

Virtual imaging trials improved the transparency and reliability of AI systems in COVID-19 imaging

https://github.com/2023-MindSpore-1/ms-code-118

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

https://github.com/2023-MindSpore-1/ms-code-211/tree/main/dnet_nas

D-Net: Siamese based Network with Mutual Attention for Volume Alignment

https://github.com/2023-MindSpore-1/ms-code-220/tree/main/EDCN

EDCNN: Edge enhancement-based Densely Connected Network with Compound Loss for Low-Dose CT Denoising

https://github.com/2023-MindSpore-4/Code3/tree/main/dpn

DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification

https://github.com/AeRabelais/synthetic_ct

Improving Tuberculosis (TB) Prediction using Synthetically Generated Computed Tomography (CT) Images

https://github.com/AmirMoMo/COVID-19

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

https://github.com/ArenCarpenter/Covid_CT_Classification

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

https://github.com/BIYTC/mobilenet_maskrcnn

MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation

https://github.com/BecauseOfGAN/capston

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

https://github.com/BecauseOfGAN/capstone

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

https://github.com/Ben-USC/covid-19-USCD

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

https://github.com/Burf/tfdetection

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

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