Segmentation Based Approach
Segmentation-based approaches aim to partition images into meaningful regions, facilitating analysis and interpretation across diverse fields. Current research emphasizes the development and application of advanced deep learning architectures, including U-Net variants, transformers (like Mamba), and foundation models (like SAM), often combined with innovative loss functions and data augmentation techniques to address challenges such as class imbalance and limited annotated data. These methods are proving impactful in various applications, from medical image analysis (e.g., tumor detection, organ segmentation) and remote sensing (e.g., crop field mapping, flood detection) to other domains requiring precise object delineation. The ongoing focus is on improving accuracy, efficiency, and explainability, particularly in scenarios with scarce or heterogeneous data.
Papers - Page 28
Segmentation of the veterinary cytological images for fast neoplastic tumors diagnosis
Jakub Grzeszczyk, Michał Karwatowski, Daria Łukasik, Maciej Wielgosz, Paweł Russek, Szymon Mazurek, Jakub Caputa, Rafał Frączek, Anna Śmiech+5Segmentation and Vascular Vectorization for Coronary Artery by Geometry-based Cascaded Neural Network
Xiaoyu Yang, Lijian Xu, Simon Yu, Qing Xia, Hongsheng Li, Shaoting Zhang
Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation
Zihao Li, Pan Gao, Hui Yuan, Ran Wei, Manoranjan PaulAdaptive-Mask Fusion Network for Segmentation of Drivable Road and Negative Obstacle With Untrustworthy Features
Zhen Feng, Yuchao Feng, Yanning Guo, Yuxiang Sun