Medical Image
Medical image analysis focuses on extracting meaningful information from various imaging modalities (e.g., CT, MRI, X-ray) to improve diagnosis and treatment planning. Current research emphasizes developing robust and efficient algorithms, often employing convolutional neural networks (CNNs), transformers, and diffusion models, to address challenges like data variability, limited annotations, and privacy concerns. These advancements are crucial for improving the accuracy and speed of medical image analysis, leading to better patient care and accelerating medical research.
Papers
PrepNet: A Convolutional Auto-Encoder to Homogenize CT Scans for Cross-Dataset Medical Image Analysis
Mohammadreza Amirian, Javier A. Montoya-Zegarra, Jonathan Gruss, Yves D. Stebler, Ahmet Selman Bozkir, Marco Calandri, Friedhelm Schwenker, Thilo Stadelmann
PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation
Guotai Wang, Xiangde Luo, Ran Gu, Shuojue Yang, Yijie Qu, Shuwei Zhai, Qianfei Zhao, Kang Li, Shaoting Zhang
Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network
Li Sun, Ke Yu, Kayhan Batmanghelich
Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users
Ana Lucic, Sheeraz Ahmad, Amanda Furtado Brinhosa, Vera Liao, Himani Agrawal, Umang Bhatt, Krishnaram Kenthapadi, Alice Xiang, Maarten de Rijke, Nicholas Drabowski
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images
Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu
Augment like there's no tomorrow: Consistently performing neural networks for medical imaging
Joona Pohjonen, Carolin Stürenberg, Atte Föhr, Reija Randen-Brady, Lassi Luomala, Jouni Lohi, Esa Pitkänen, Antti Rannikko, Tuomas Mirtti