2021/09/02 by Seyed Hossein Mirjahanmardi, Seyed H. Mirjahanmardi, Samir Mitha +9
Computer Science · Engineering · #AI in cancer detection #Digital Imaging for Blood Diseases #FOS: Electrical engineering #Image Retrieval and Classification Techniques #Image and Video Processing (eess.IV) #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2109.01526
openalex publication_date 2021/09/02 · arxiv created 2021/10/21 · arxiv updated 2021/10/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The difficulty of detecting mitosis and its similarity to non-mitosis objects has remained a challenge in computational pathology. The lack of publicly available data has added more complexity. Deep learning algorithms have shown potentials in mitosis detection tasks. However, they face challenges when applied to pathology images with dense medium and diverse dataset. This paper introduces an optimized UV-Net architecture, developed to focus on mitosis details with high-resolution through feature preservation. Stain normalization methods are used to generalize the trained network. An F1 score of 0.6721 is achieved using this network.