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/faebstn96/helix2fan

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On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Setting

https://github.com/fanxiaohong/mvms-rcn

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MVMS-RCN: A Dual-Domain Unfolding CT Reconstruction with Multi-sparse-view and Multi-scale Refinement-correction

https://github.com/ferchonavarro/shape_aware_segmentation

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Shape-Aware Complementary-Task Learning for Multi-Organ Segmentation

https://github.com/fidsinn/sporttaskme22

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Fine-Grained Action Detection with RGB and Pose Information using Two Stream Convolutional Networks

https://github.com/firasgit/medicaldiffusion

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Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation

https://github.com/fitushar/CVIT_ReviCOVID19

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

https://github.com/fitushar/multi-label-annotation-text-reports-body-CT

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Multi-Label Annotation of Chest Abdomen Pelvis Computed Tomography Text Reports Using Deep Learning

https://github.com/fitushar/multi-label-weakly-supervised-classification-of-body-ct

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Classification of Multiple Diseases on Body CT Scans using Weakly Supervised Deep Learning

https://github.com/grey891/End2EndLandmarks

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An End-to-end Deep Learning Approach for Landmark Detection and Matching in Medical Images

https://github.com/guo-stone/mambamorph

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MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration

https://github.com/hanyoseob/framing-u-net

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Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT

https://github.com/hao1635/ascon

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ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising

https://github.com/hao1635/leda

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Low-dose CT Denoising with Language-engaged Dual-space Alignment

https://github.com/hao1635/lit-former

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LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring

https://github.com/hdusyz/csf-net

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CSF-Net: Cross-Modal Spatiotemporal Fusion Network for Pulmonary Nodule Malignancy Predicting

https://github.com/heyblackC/DFMIR

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Unsupervised Multi-Modal Medical Image Registration via Discriminator-Free Image-to-Image Translation

https://github.com/hilab-git/pf-net

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Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis from Lung CT Scans with Multi-Scale Guided Dense Attention

https://github.com/hilab-git/segrap2023

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SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

https://github.com/hongwang01/ACDNet

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Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction

https://github.com/hongwang01/indudonet_plus

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InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images

https://github.com/hongwang01/mepnet

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MEPNet: A Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Images

https://github.com/hongwang01/oscnet

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Orientation-Shared Convolution Representation for CT Metal Artifact Learning

https://github.com/hwei-hw/generalist_vision_foundation_models_for_medical_imaging

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Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation

https://github.com/hxf1228/dtcnn_elm_lnd

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Deep Transfer Convolutional Neural Network and Extreme Learning Machine for Lung Nodule Diagnosis on CT images

https://github.com/iWeisskohl/Evidential-neural-network-for-lymphoma-segmentation

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Lymphoma segmentation from 3D PET-CT images using a deep evidential network

https://github.com/ibrahimethemhamamci/generatect

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GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes

https://github.com/idu-cvlab/cov19d_3rd

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COVID-19 Detection Using Segmentation, Region Extraction and Classification Pipeline

https://github.com/ieee8023/ct-counterfactuals

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Explaining 3D Computed Tomography Classifiers with Counterfactuals

https://github.com/ilmari99/data-efficient-lact

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Data-Efficient Limited-Angle CT Using Deep Priors and Regularization

https://github.com/imatge-upc/liverseg-2017-nipsws

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Detection-aided liver lesion segmentation using deep learning

https://github.com/ituvisionlab/Uncertainty-Based-Dynamic-Graph-Neighborhoods

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Uncertainty-Based Dynamic Graph Neighborhoods For Medical Segmentation

https://github.com/jcwang123/DM2TNet

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Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19

https://github.com/jdsobek/medyolo

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MedYOLO: A Medical Image Object Detection Framework

https://github.com/jeya-maria-jose/KiU-Net-pytorch

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KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation

https://github.com/jianqingzheng/path_planning_for_FEVAR

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3D Path Planning from a Single 2D Fluoroscopic Image for Robot Assisted Fenestrated Endovascular Aortic Repair

https://github.com/jiayangshi/SR4ZCT

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SR4ZCT: Self-supervised Through-plane Resolution Enhancement for CT Images with Arbitrary Resolution and Overlap

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