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/99991/htc2022-tud-hhu-version-1

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Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic Data

https://github.com/AIM-KannLab/TissUNet

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TissUnet: Improved Extracranial Tissue and Cranium Segmentation for Children through Adulthood

https://github.com/AugustDS/synthetic-medical-benchmark

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Overcoming Barriers to Data Sharing with Medical Image Generation: A Comprehensive Evaluation

https://github.com/BIMCV-CSUSP/BIMCV-COVID-19

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BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients

https://github.com/Behzad-Amanpour/medical_image_pre-processing/tree/main/Body_Background_Separation

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Separation of Body and Background in Radiological Images. A Practical Python Code

https://github.com/BioIntelligence-Lab/CT-Embedding-Bias

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Demographic Predictability in 3D CT Foundation Embeddings

https://github.com/BioIntelligence-Lab/VAE-Adversarial-Debiasing

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Towards Fair Medical AI: Adversarial Debiasing of 3D CT Foundation Embeddings

https://github.com/Captain-Hong/UDA-via-joint-adversarial-learning-and-self-learning

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Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning

https://github.com/DIAL-RPI/COVID19-ICUPrediction

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Integrative Analysis for COVID-19 Patient Outcome Prediction

https://github.com/DengPingFan/Inf-Net

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Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

https://github.com/Heroandzhang/dataset_process

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A Cross Spatio-Temporal Pathology-based Lung Nodule Dataset

https://github.com/HiLab-git/GLFC

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GLFC: Unified Global-Local Feature and Contrast Learning with Mamba-Enhanced UNet for Synthetic CT Generation from CBCT

https://github.com/HiLab-git/SepNet

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Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss

https://github.com/HzFu/COVID19_imaging_AI_paper_list

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Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

https://github.com/ICT-MIRACLE-lab/CTSpine1K

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

https://github.com/IDU-CVLab/COV19D_2nd

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COVID-19 Detection Using Transfer Learning Approach from Computed Tomography Images

https://github.com/KarenUllrich/pytorch-backprojection

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Differentiable probabilistic models of scientific imaging with the Fourier slice theorem

https://github.com/L0SG/grouped-ssd-pytorch

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Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images

https://github.com/LINGYUNFDU/MSSU-Net

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Multi-Scale Supervised 3D U-Net for Kidneys and Kidney Tumor Segmentation

https://github.com/Luffy03/FreeTumor

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FreeTumor: Large-Scale Generative Tumor Synthesis in Computed Tomography Images for Improving Tumor Recognition

https://github.com/M3DV/AlignShift

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Asymmetric 3D Context Fusion for Universal Lesion Detection

https://github.com/MASILab/ALARM

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Fully Automatic Liver Attenuation Estimation Combing CNN Segmentation and Morphological Operations

https://github.com/MASILab/QA_tool

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Technical Report: Quality Assessment Tool for Machine Learning with Clinical CT

https://github.com/MIPT-Oulu/DeepWrist

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Critical Evaluation of Deep Neural Networks for Wrist Fracture Detection

https://github.com/MMianyong/-PedAbdSeg-

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Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy

https://github.com/Masaaki-75/proct

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Prompted Contextual Transformer for Incomplete-View CT Reconstruction

https://github.com/MrGiovanni/UNetPlusPlus

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

https://github.com/NarcissusEx/CuNeRF

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CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution

https://github.com/ParnianA/LDCT-COVID

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Human-level COVID-19 Diagnosis from Low-dose CT Scans Using a Two-stage Time-distributed Capsule Network

https://github.com/Project-MONAI/VISTA

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VISTA3D: Versatile Imaging SegmenTation and Annotation model for 3D Computed Tomography

https://github.com/Project-MONAI/research-contributions/tree/master/SwinUNETR

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Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis

https://github.com/Project-MONAI/tutorials/tree/main/generation/maisi

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MAISI: Medical AI for Synthetic Imaging

https://github.com/Pulkit-Khandelwal/medical-mldg-seg

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Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imaging

https://github.com/RPIDIAL/BI-Mamba

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Cardiovascular Disease Detection from Multi-View Chest X-rays with BI-Mamba

https://github.com/Ruixxxx/SGDA

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SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention

https://github.com/STNLd2/FBPAug

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Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation

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