Classification of tissue regions in histopathological images: Comparison between pre-trained convolutional neural networks and local binary patterns variants
Research output: Contribution to book/Conference proceedings/Anthology/Report › Chapter in book/Anthology/Report › Contributed › peer-review
Contributors
Abstract
The identification of tissue regions within histopathological images represents a fundamental step for diagnosis, patient stratification and follow-up. However, the huge amount of image data made available by the ever improving whole-slide imaging devices gives rise to a bottleneck in manual, microscopy-based evaluation. Furthermore, manual procedures generally show a significant intra- and/or inter-observer variability. In this scenario the objective of this chapter is to investigate the effectiveness of image features from last-generation, pre-trained convolutional networks against variants of Local Binary Patterns for classifying tissue sub-regions into meaningful classes such as epithelium, stroma, lymphocytes and necrosis. Experimenting with seven datasets of histopathological images we show that both classes of methods can be quite effective for the task, but with a noticeable superiority of descriptors based on convolutional neural networks. In particular, we show that these can be seamlessly integrated with standard classifiers (e.g. Support Vector Machines) to attain overall discrimination accuracy between 95 and 99%.
Details
Original language | English |
---|---|
Title of host publication | Deep Learners and Deep Learner Descriptors for Medical Applications |
Editors | Loris Nanni, Sheryl Brahnam, Rick Brattin, Stefano Ghidoni, Lakhmi C. Jain |
Publisher | Springer |
Pages | 95-115 |
Number of pages | 21 |
ISBN (electronic) | 978-3-030-42750-4 |
ISBN (print) | 978-3-030-42748-1 |
Publication status | Published - 2020 |
Peer-reviewed | Yes |
Externally published | Yes |
Publication series
Series | Intelligent Systems Reference Library |
---|---|
Volume | 186 |
ISSN | 1868-4394 |