CT Image

Computed tomography (CT) imaging produces detailed 3D images of the body, crucial for diagnosis and treatment planning across various medical specialties. Current research emphasizes improving CT image analysis through deep learning, focusing on architectures like U-Nets, Vision Transformers, and diffusion models for tasks such as organ segmentation, lesion detection, and image enhancement. These advancements aim to increase diagnostic accuracy, improve treatment planning, reduce radiation exposure, and enable more efficient workflows in clinical settings.

Papers

December 20, 2021

Deep Co-supervision and Attention Fusion Strategy for Automatic COVID-19 Lung Infection Segmentation on CT Images
Haigen Hu, Leizhao Shen, Qiu Guan, Xiaoxin Li, Qianwei Zhou, Su Ruan
CT Image Edge Learning Co Training Boundary Aware Attention Fusion Novel Segmentation Co Supervision

December 9, 2021

Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images
Qinghao Ye, Yuan Gao, Weiping Ding, Zhangming Niu, Chengjia Wang, Yinghui Jiang, Minhao Wang, Evandro Fei Fang, Wade Menpes-Smith, Jun Xia, Guang Yang
Covid 19 CT Image Weakly Supervised Learning Information Fusion

December 6, 2021

Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning
Sankaran Iyer, Alan Blair, Laughlin Dawes, Daniel Moses, Christopher White, Arcot Sowmya
Reinforcement Learning Semi Supervised Learning Imitation Learning Semi Supervised Constructive Approach Medical Imaging CT Image Multi Organ

November 22, 2021

Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images
Kenan Morani, Devrim Unay
Deep Learning Convolutional Neural Network Image Classification Covid 19 CT Image Convolutional Neural Network Model

November 21, 2021

DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction
Ce Wang, Kun Shang, Haimiao Zhang, Qian Li, Yuan Hui, S. Kevin Zhou
Sparse View CT Image Domain Transformer

November 18, 2021

Deep neural networks-based denoising models for CT imaging and their efficacy
Prabhat KC, Rongping Zeng, M. Mehdi Farhangi, Kyle J. Myers
Computed Tomography CT Image Low Dose Neural Network Output Deep Learning Based Denoising

November 17, 2021

Segmentation of Lung Tumor from CT Images using Deep Supervision
Farhanaz Farheen, Md. Salman Shamil, Nabil Ibtehaz, M. Sohel Rahman
Segmentation Based Approach CT Image Lung Cancer Deep Supervision Discrete Wavelet Transform Tumor Detection Lung Originated Tumor Segmentation

November 16, 2021

CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images
Azael M. Sousa, Fabiano Reis, Rachel Zerbini, João L. D. Comba, Alexandre X. Falcão
Convolutional Neural Network Data Detection CT Image Early Detection Computed Tomography Reconstruction Convolutional Filter Chest CT Planar Marker Early Stage

November 3, 2021

WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image
Xiangde Luo, Wenjun Liao, Jianghong Xiao, Jieneng Chen, Tao Song, Xiaofan Zhang, Kang Li, Dimitris N. Metaxas, Guotai Wang, Shaoting Zhang
New Benchmark Large Scale Dataset CT Image Abdominal Organ Segmentation Clinical Application Deep Learning Based Medical Organ Annotation

CT Image

Papers

Deep Co-supervision and Attention Fusion Strategy for Automatic COVID-19 Lung Infection Segmentation on CT Images

Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images

Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning

Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images

DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstruction

Deep neural networks-based denoising models for CT imaging and their efficacy

Segmentation of Lung Tumor from CT Images using Deep Supervision

CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images

WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image