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
Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images
Yu Cai, Hao Chen, Xin Yang, Yu Zhou, Kwang-Ting Cheng
Adapting Pretrained Vision-Language Foundational Models to Medical Imaging Domains
Pierre Chambon, Christian Bluethgen, Curtis P. Langlotz, Akshay Chaudhari
Efficient Medical Image Assessment via Self-supervised Learning
Chun-Yin Huang, Qi Lei, Xiaoxiao Li
Medical Image Captioning via Generative Pretrained Transformers
Alexander Selivanov, Oleg Y. Rogov, Daniil Chesakov, Artem Shelmanov, Irina Fedulova, Dmitry V. Dylov
Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification
Peng Jiang, Juan Liu, Lang Wang, Zhihui Ynag, Hongyu Dong, Jing Feng
CCTCOVID: COVID-19 Detection from Chest X-Ray Images Using Compact Convolutional Transformers
Abdolreza Marefat, Mahdieh Marefat, Javad Hasannataj Joloudari, Mohammad Ali Nematollahi, Reza Lashgari
RepsNet: Combining Vision with Language for Automated Medical Reports
Ajay Kumar Tanwani, Joelle Barral, Daniel Freedman
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