In this study, we demonstrate the efficacy of scoring statistics derived from a medial axis transform, for differentiating tumor and non-tumor nuclei, in malignant breast tumor histopathology images. Characterizing nuclei shape is a crucial part of diagnosing breast tumors for human doctors, and these scoring metrics may be integrated into machine perception algorithms which aggregate nuclei information across a region to label whole breast lesions. In particular, we present a low-dimensional representation capturing characteristics of a skeleton extracted from nuclei. We show that this representation outperforms both prior morphological features, as well as CNN features, for classification of tumors. Nuclei and region scoring algorithms such as the one presented here can aid pathologists in the diagnosis of breast tumors.
|Title of host publication||Computer Vision – ECCV 2020 Workshops, Proceedings|
|Editors||Adrien Bartoli, Andrea Fusiello|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||14|
|Publication status||Published - 2020|
|Event||Workshops held at the 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom|
Duration: 2020 Aug 23 → 2020 Aug 28
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||Workshops held at the 16th European Conference on Computer Vision, ECCV 2020|
|Period||20/8/23 → 20/8/28|
Bibliographical notePublisher Copyright:
© 2020, Springer Nature Switzerland AG.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)