Predicting Residual Cancer Burden In A Triple Negative Breast Cancer Cohort
- Resource Type
- Conference
- Authors
- Naylor, Peter; Boyd, Joseph; Lae, Marick; Reyal, Fabien; Walter, Thomas
- Source
- 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Biomedical Imaging (ISBI 2019), 2019 IEEE 16th International Symposium on. :933-937 Apr, 2019
- Subject
- Bioengineering
Feature extraction
Breast cancer
Prognostics and health management
Task analysis
Data mining
Chemotherapy
Breast Cancer
Computer-aided detection and diagnosis
Deep Learning
Digital Pathology
Histopathology
Triple Negative Breast Cancer
- Language
- ISSN
- 1945-8452
Analysis and interpretation of stained histopathology sections is one of the main tools in cancer diagnosis and prognosis. In addition to the information which is typically extracted by trained pathologists, there is also information that is not yet exploited, simply because we do not yet understand the impact of all cellular and tissular features that could be predictive of outcome. In this paper, we address a question that can currently not be solved by pathologists: the prediction of treatment efficiency for Triple Negative Breast Cancer (TNBC) patients from biopsy data.