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/ayanglab/xai_covid-19

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Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels

https://github.com/baai-dcai/segvol

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SegVol: Universal and Interactive Volumetric Medical Image Segmentation

https://github.com/baidu-research/GPN

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Detecting Lesion Bounding Ellipses With Gaussian Proposal Networks

https://github.com/biomedia-mbzuai/pecon

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PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism Diagnosis

https://github.com/biomedical-data-analysis-laboratory/4d-mj-net

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CT Perfusion is All We Need: 4D CNN Segmentation of Penumbra and Core in Patients With Suspected Ischemic Stroke

https://github.com/bobby-he/uncertainr

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UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography

https://github.com/bodokaiser/mrtoct-pytorch

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MRI to CT Translation with GANs

https://github.com/bodokaiser/mrtoct-scripts

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MRI to CT Translation with GANs

https://github.com/bodokaiser/mrtoct-tensorflow

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MRI to CT Translation with GANs

https://github.com/brack-wang/x-field

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X-Field: A Physically Grounded Representation for 3D X-ray Reconstruction

https://github.com/caiziruo/nf-ula

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NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems

https://github.com/ccp-eva/sporttaskme21

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Spatio-Temporal CNN baseline method for the Sports Video Task of MediaEval 2021 benchmark

https://github.com/ccp-eva/sporttaskme22

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Baseline Method for the Sport Task of MediaEval 2022 with 3D CNNs using Attention Mechanisms

https://github.com/cesaremagnetti/automaticusnavigation

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Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style Transfer

https://github.com/chaoyi-wu/gpt-4v_medical_evaluation

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Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosis

https://github.com/chengyinlee/focalunetr

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FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images

https://github.com/chengyinlee/mulmodseg_2024

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MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training

https://github.com/chycxyzd/ldfc

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A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation

https://github.com/cicwi/WalnutReconstructionCodes

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A Cone-Beam X-Ray CT Data Collection designed for Machine Learning

https://github.com/constantinseibold/chestxrayanatomysegmentation

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Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling

https://github.com/cpeng93/XraySyn

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XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors

https://github.com/csqvictory/sdct-gan

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SdCT-GAN: Reconstructing CT from Biplanar X-Rays with Self-driven Generative Adversarial Networks

https://github.com/deep-voxel/automatic_pulmonary_lobe_segmentation_using_deep_learning

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Automatic Pulmonary Lobe Segmentation Using Deep Learning

https://github.com/deepmind/tcia-ct-scan-dataset

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Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy

https://github.com/deepxuan/dn-dp

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Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising

https://github.com/deepxuan/dudodp-mar

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Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains

https://github.com/dek924/perx2ct

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Perspective Projection-Based 3D CT Reconstruction from Biplanar X-rays

https://github.com/devalab/swinfuse

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Self-Supervised Modality-Agnostic Pre-Training of Swin Transformers

https://github.com/devangelista2/DeepGuess

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Deep Guess acceleration for explainable image reconstruction in sparse-view CT

https://github.com/digitalhealthcarelab/25multimodalfoundationmodel

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MEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer Analysis

https://github.com/dmytro-shvetsov/counterfactual-search

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COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical images

https://github.com/dootmaan/pfseg-full

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Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance

https://github.com/dysthymiac/syntheticlogs.jl

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Towards synthetic generation of realistic wooden logs

https://github.com/endiqq/Multi-Feature-Semi-Supervised-Learning-for-COVID-19-CXR-Images

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Multi-Feature Semi-Supervised Learning for COVID-19 Diagnosis from Chest X-ray Images

https://github.com/esizikova/medsynth_public

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Improving Computed Tomography (CT) Reconstruction via 3D Shape Induction

https://github.com/fabianaltekrueger/patchnr

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PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization

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