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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/jiesihu/decor-net

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DECOR-NET: A COVID-19 Lung Infection Segmentation Network Improved by Emphasizing Low-level Features and Decorrelating Features

https://github.com/jmaces/aapm-ct-challenge

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Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning

https://github.com/jonathanfrawley/mednerf

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MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray

https://github.com/juglab/FourierImageTransformer

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Fourier Image Transformer

https://github.com/junma11/flare

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Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge

https://github.com/jwcalder/ct-surfacing

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The Batch Artifact Scanning Protocol: A new method using computed tomography (CT) to rapidly create three-dimensional models of objects from large collections en masse

https://github.com/kamruleee51/CVR-Net

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CVR-Net: A deep convolutional neural network for coronavirus recognition from chest radiography images

https://github.com/keisuke-uemura/CT-Intensity-Calibration-Phantom-Segmentation

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Automated segmentation of an intensity calibration phantom in clinical CT images using a convolutional neural network

https://github.com/kskim-phd/vmpr-uad

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3D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomography

https://github.com/kvgandikota/robustness-low-dose-ct

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Evaluating Adversarial Robustness of Low dose CT Recovery

https://github.com/kylekma/X2CT

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X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks

https://github.com/lfz/DSB2017

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Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network

https://github.com/liaohaofu/adn

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Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

https://github.com/liaw05/stmixer

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Siamese Encoder-based Spatial-Temporal Mixer for Growth Trend Prediction of Lung Nodules on CT Scans

https://github.com/lilygeorgescu/mhca

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Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution

https://github.com/lilygoli/longitudinalCOVID

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Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs

https://github.com/lingchm/medassist-liver-cancer

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SmoothSegNet: A Global-Local Framework for Liver Tumor Segmentation with Clinical KnowledgeInformed Label Smoothing

https://github.com/lizrzr/tosm_3d-inverse-problem

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Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse Problems

https://github.com/ljollans/trunet

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Vision Transformers increase efficiency of 3D cardiac CT multi-label segmentation

https://github.com/ljwztc/clip-driven-universal-model

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Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography

https://github.com/longzilicart/gloredi

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Learning to Distill Global Representation for Sparse-View CT

https://github.com/longzilicart/quad-net

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Quad-Net: Quad-domain Network for CT Metal Artifact Reduction

https://github.com/lppllppl920/EndoscopyDepthEstimation-Pytorch

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Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods

https://github.com/lucasxteixeira/covid19-segmentation-paper

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Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images

https://github.com/luukboulogne/stoic2021-baseline

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The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data

https://github.com/lzhengchun/tomotx

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Masked Sinogram Model with Transformer for ill-Posed Computed Tomography Reconstruction: a Preliminary Study

https://github.com/m3dv/ribseg

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RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans

https://github.com/maggiebasta/lung-cancer-thesis

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Automated Segmentation and Recurrence Risk Prediction of Surgically Resected Lung Tumors with Adaptive Convolutional Neural Networks

https://github.com/marsggbo/miccai2022-emars

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Evolutionary Multi-objective Architecture Search Framework: Application to COVID-19 3D CT Classification

https://github.com/masaaki-75/freeseed

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FreeSeed: Frequency-band-aware and Self-guided Network for Sparse-view CT Reconstruction

https://github.com/masaaki-75/rise-mar

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Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction

https://github.com/masilab/c-slicegen

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Reducing Positional Variance in Cross-sectional Abdominal CT Slices with Deep Conditional Generative Models

https://github.com/masilab/da_ct_muscle_seg

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Single Slice Thigh CT Muscle Group Segmentation with Domain Adaptation and Self-Training

https://github.com/masilab/dcc_cl

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Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation

https://github.com/masilab/lmsignatures

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Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification

https://github.com/masilab/s-efov

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Body Composition Assessment with Limited Field-of-view Computed Tomography: A Semantic Image Extension Perspective

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