A robust segmentation method is developed to delineate region-of-interests (eg, cells) accurately, using hierarchical voting and repulsive active contour.

Computer-aided diagnosis of medical images requires thorough analysis of image details. For example, examining all cells enables fine-grained cate-.

... In Zhang et al. (2015) , a robust segmentation method is developed to accurately delineate ROI (eg, cells) using hierarchical voting and repelling active ...

Computer-aided diagnosis of medical images requires thorough analysis of image details. For example, examining all cells enables fine-grained categorization.

Our method has achieved promising performance, i.e., 87.3% accuracy and 1.68 seconds by searching among half-million cells. Fine-Grained Histopathological Image ...

Specifically, a robust segmentation method is developed to delineate region-of-interests (e.g., cells) accurately, using hierarchical voting and repulsive ...

In this paper, we propose a robust and scalable solution to enable such analysis in a real-time fashion. Specifically, a robust segmentation method is developed ...

Our solution includes two important modules, robust cell segmentation and large-scale cell retrieval. Specifically, segmentation module provides automatic ...

Particularly, we focus on the automatic analysis of histopathological images, and propose a scalable image retrieval framework with high-dimensional features ...

Fine-grained histopathological image analysis via robust segmentation and large-scale retrieval · Robust Segmentation of Overlapping Cells in Histopathology ...