Image Analysis
Image analysis, particularly in medical applications, focuses on developing automated methods for extracting meaningful information from images, aiding diagnosis and treatment planning. Current research emphasizes improving model robustness and generalizability across diverse datasets and imaging conditions, employing architectures like U-Nets, Vision Transformers, and Generative Adversarial Networks, often incorporating techniques like self-supervised learning and contrastive learning. These advancements hold significant potential for improving diagnostic accuracy, streamlining workflows, and accelerating research in various fields, including pathology, radiology, and ophthalmology.
Papers
Towards better understanding and better generalization of few-shot classification in histology images with contrastive learning
Jiawei Yang, Hanbo Chen, Jiangpeng Yan, Xiaoyu Chen, Jianhua Yao
A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements
Jiawei Zhang, Chen Li, Md Mamunur Rahaman, Yudong Yao, Pingli Ma, Jinghua Zhang, Xin Zhao, Tao Jiang, Marcin Grzegorzek
REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening
Huihui Fang, Fei Li, Junde Wu, Huazhu Fu, Xu Sun, Jaemin Son, Shuang Yu, Menglu Zhang, Chenglang Yuan, Cheng Bian, Baiying Lei, Benjian Zhao, Xinxing Xu, Shaohua Li, Francisco Fumero, José Sigut, Haidar Almubarak, Yakoub Bazi, Yuanhao Guo, Yating Zhou, Ujjwal Baid, Shubham Innani, Tianjiao Guo, Jie Yang, José Ignacio Orlando, Hrvoje Bogunović, Xiulan Zhang, Yanwu Xu