A Study on Annotation Efficient Learning Methods for Segmentation in Prostate Histopathological Images

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Costa, P (Author) Other; Aurélio Campilho (Author) FEUP; Jaime S Cardoso (Author) FEUP

Conference proceedings International

Title: CIARP

Pages: 55-64

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00V-YE1

DOI: 10.1007/978-3-030-93420-0_6

Abstract (EN): Cancer is a leading cause of death worldwide. The detection and diagnosis of most cancers are confirmed by a tissue biopsy that is analyzed via the optic microscope. These samples are then scanned to giga-pixel sized images for further digital processing by pathologists. An automated method to segment the malignant regions of these images could be of great interest to detect cancer earlier and increase the agreement between specialists. However, annotating these giga-pixel images is very expensive, time-consuming and error-prone. We evaluate 4 existing annotation efficient methods, including transfer learning and self-supervised learning approaches. The best performing approach was to pretrain a model to colourize a grayscale histopathological image and then finetune that model on a dataset with manually annotated examples. This method was able to improve the Intersection over Union from 0.2702 to 0.3702.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9