Papers with Code 아카이브

Computed Tomography (CT)

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 0
보존된 평가 기록: 0

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/SamMas-hub/Fat-Segmentation-in-MR-using-domain-adaptation

공식 코드

Adipose Tissue Segmentation in Unlabeled Abdomen MRI using Cross Modality Domain Adaptation

https://github.com/ShahinSHH/COVID-CAPS

공식 코드

COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images

https://github.com/ShahinSHH/COVID-FACT

공식 코드

COVID-FACT: A Fully-Automated Capsule Network-based Framework for Identification of COVID-19 Cases from Chest CT scans

https://github.com/ShahinSHH/CT-CAPS

공식 코드

CT-CAPS: Feature Extraction-based Automated Framework for COVID-19 Disease Identification from Chest CT Scans using Capsule Networks

https://github.com/Subhadip-1/unrolling_meets_data_driven_regularization

공식 코드

End-to-end reconstruction meets data-driven regularization for inverse problems

https://github.com/Synho/windows_optimization

공식 코드

Practical Window Setting Optimization for Medical Image Deep Learning

https://github.com/TUM-AIMED/2.5DAttention

공식 코드

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

https://github.com/UBC-CIC/COVID19-L3-Net

공식 코드

Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients

https://github.com/UCSB-VRL/NPH_Prediction

공식 코드

Automated Segmentation of CT Scans for Normal Pressure Hydrocephalus

https://github.com/VinBDI-MedicalImagingTeam/midl2020-cnnlstm-ich

공식 코드

A CNN-LSTM Architecture for Detection of Intracranial Hemorrhage on CT scans

https://github.com/Wotaker/Effective-Connectivity-Reservoir-Computing

공식 코드

End-to-end Stroke imaging analysis, using reservoir computing-based effective connectivity, and interpretable Artificial intelligence

https://github.com/XiaoYunZhou27/ACNN

공식 코드

ACNN: a Full Resolution DCNN for Medical Image Segmentation

https://github.com/XiaoweiXu/ImageCHD-A-3D-Computed-Tomography-Image-Dataset-for-Classification-of-Congenital-Heart-Disease

공식 코드

ImageCHD: A 3D Computed Tomography Image Dataset for Classification of Congenital Heart Disease

https://github.com/YaoZhang93/MAML

공식 코드

Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation

https://github.com/aanandt/enhancing-covid-19-severity-analysis-through-ensemble-methods

공식 코드

Enhancing COVID-19 Severity Analysis through Ensemble Methods

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

공식 코드

Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging

https://github.com/abrilcf/mednerf

공식 코드

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

https://github.com/adriapr/crowdairway

공식 코드

Crowdsourcing Airway Annotations in Chest Computed Tomography Images

https://github.com/agis85/anatomy_modality_decomposition

공식 코드

Disentangled Representation Learning in Cardiac Image Analysis

https://github.com/agnalt/window-shifting

공식 코드

View it like a radiologist: Shifted windows for deep learning augmentation of CT images

https://github.com/ai-vbrain/pymeal

공식 코드

pyMEAL: A Multi-Encoder Augmentation-Aware Learning for Robust and Generalizable Medical Image Translation

https://github.com/ajinkyakadu/DynamicXRayCT

공식 코드

Single-shot Tomography of Discrete Dynamic Objects

https://github.com/aleemsidra/stoic2021-baseline-finalphase-main

공식 코드

An Ensemble Deep Learning Approach for COVID-19 Severity Prediction Using Chest CT Scans

https://github.com/aleesuss/c19

공식 코드

Explainable and Lightweight Model for COVID-19 Detection Using Chest Radiology Images

https://github.com/alibaba-damo-academy/med_query

공식 코드

Med-Query: Steerable Parsing of 9-DoF Medical Anatomies with Query Embedding

https://github.com/alibaba-damo-academy/pixel-lesion-patient-network

공식 코드

Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network

https://github.com/alibaba-damo-academy/self-supervised-anatomical-embedding-v2

공식 코드

SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images

https://github.com/alperelli/MCAOL

공식 코드

Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction

https://github.com/analysiscenter/radio

공식 코드

Clearing noisy annotations for computed tomography imaging

https://github.com/andreped/livermask

공식 코드

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

https://github.com/anissa218/autopet_nnunet

공식 코드

Autopet Challenge 2023: nnUNet-based whole-body 3D PET-CT Tumour Segmentation

https://github.com/anjany/verse

공식 코드

A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data

https://github.com/asHauptmann/multiscale

공식 코드

Multi-Scale Learned Iterative Reconstruction

https://github.com/ashkanpakzad/AirQuant

공식 코드

Evaluation of automated airway morphological quantification for assessing fibrosing lung disease

https://github.com/ashkanpakzad/atn

공식 코드

Airway measurement by refinement of synthetic images improves mortality prediction in idiopathic pulmonary fibrosis

https://github.com/astlian9/sam_support

공식 코드

SAM2-SGP: Enhancing SAM2 for Medical Image Segmentation via Support-Set Guided Prompting

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