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/sulam-group/mil_ich

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Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT

https://github.com/svaishnavi411/multimodal-recurrence

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Multimodal fusion of imaging and genomics for lung cancer recurrence prediction

https://github.com/synthetaic/COVID19-IntraModel-Variability

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Intra-model Variability in COVID-19 Classification Using Chest X-ray Images

https://github.com/synthrad2023/preprocessing

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SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy

https://github.com/sypsyp97/Trainable-Fourier-Series

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Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier Series

https://github.com/sypsyp97/eagle_loss

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EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstruction

https://github.com/tayebiarasteh/dp_cxr

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Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging

https://github.com/tbwa233/polycl

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Domain and Task-Focused Example Selection for Data-Efficient Contrastive Medical Image Segmentation

https://github.com/tbwa233/sam-mix

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Annotation-Efficient Task Guidance for Medical Segment Anything

https://github.com/thanhhff/CPDM

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CT to PET Translation: A Large-scale Dataset and Domain-Knowledge-Guided Diffusion Approach

https://github.com/thu-cvlab/jmedsegv2

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The changing rule of human bone density with aging based on a novel definition and mensuration of bone density with computed tomography

https://github.com/tianyuan1wang/seqanglerl

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Sequential Experimental Design for X-Ray CT Using Deep Reinforcement Learning

https://github.com/tidorosti/reader-study_unet-processed-lung-cancer-ct

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Improving image quality of sparse-view lung tumor CT images with U-Net

https://github.com/tio-ikim/transfer-learning-across-domain-boundaries

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Why does my medical AI look at pictures of birds? Exploring the efficacy of transfer learning across domain boundaries

https://github.com/tomas-lilingfeng/sranet

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Structure Regularized Attentive Network for Automatic Femoral Head Necrosis Diagnosis and Localization

https://github.com/tonmoy-hossain/CoRLD

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CoRLD: Contrastive Representation Learning Of Deformable Shapes In Images

https://github.com/tonyckc/uda-bua

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Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty Alignment

https://github.com/tueimage/lung-nodule-msc-2018

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Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning

https://github.com/uci-cbcl/DeepEM-for-Weakly-Supervised-Detection

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DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection

https://github.com/uncbiag/2D3DFluidReg

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Fluid registration between lung CT and stationary chest tomosynthesis images

https://github.com/uncbiag/ICON

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$\texttt{GradICON}$: Approximate Diffeomorphisms via Gradient Inverse Consistency

https://github.com/uncbiag/LiftReg

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LiftReg: Limited Angle 2D/3D Deformable Registration

https://github.com/urmagicsmine/MP3D

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Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices

https://github.com/urmagicsmine/MVP-Net

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MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection

https://github.com/uw-bionlp/mimic-cxr_clinical_findings

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Event-based clinical findings extraction from radiology reports with pre-trained language model

https://github.com/vganapati/led_pvae

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Data-Driven Computational Imaging for Scientific Discovery

https://github.com/vlbthambawita/hecktor_2022_mlc

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MLC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning

https://github.com/voilalab/exact

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Accurate, provable, and fast nonlinear tomographic reconstruction: A variational inequality approach

https://github.com/voreille/ssbcnn

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3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis

https://github.com/vri-ufpr/dacov2022

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DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation Problem

https://github.com/vri-ufpr/sparkinthedarklars2021

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Spark in the Dark: Evaluating Encoder-Decoder Pairs for COVID-19 CT's Semantic Segmentation

https://github.com/wangshansong1/Triad

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Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging

https://github.com/wangwei-cmd/limited-angle-CT-reconstruction

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A model-guided deep network for limited-angle computed tomography

https://github.com/wangyunpengbio/x-recon

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X-Recon: Learning-based Patient-specific High-Resolution CT Reconstruction from Orthogonal X-Ray Images

https://github.com/whisney/dosediff

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DoseDiff: Distance-aware Diffusion Model for Dose Prediction in Radiotherapy

https://github.com/wongzbb/diffma-diffusion-mamba

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Soft Masked Mamba Diffusion Model for CT to MRI Conversion

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