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Multi-scale Deformable Transformer for the Classification of Gastric Glands: The IMGL Dataset

Barmpoutis, Panagiotis; Yuan, Jing; Waddingham, William; Ross, Christopher; Hamzeh, Kayhanian; Stathaki, Tania; Alexander, Daniel C; (2022) Multi-scale Deformable Transformer for the Classification of Gastric Glands: The IMGL Dataset. In: Ali, S and VanDerSommen, F and Papiez, BW and VanEijnatten, M and Jin, Y and Kolenbrander, I, (eds.) MICCAI Workshop on Cancer Prevention through Early Detection CaPTion 2022: Cancer Prevention Through Early Detection. (pp. pp. 24-33). Springer, Cham Green open access

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Abstract

Gastric cancer is one of the most common cancers and a leading cause of cancer-related death worldwide. Among the risk factors of gastric cancer, the gastric intestinal metaplasia (IM) has been found to increase the risk of gastric cancer and is considered as one of the precancerous lesions. Therefore, early detection of IM could allow risk stratification regarding the possibility of progression to cancer. To this end, accurate classification of gastric glands from the histological images plays an important role in the diagnostic confirmation of IM. To date, although many gland segmentation approaches have been proposed, no general model has been proposed for the identification of IM glands. Thus, in this paper, we propose a model for gastric glands’ classification. More specifically, we propose a multi-scale deformable transformer-based network for glands’ classification into normal and IM gastric glands. To evaluate the efficiency of the proposed methodology we created the IMGL dataset consisting of 1000 gland images, including both intestinal metaplasia and normal cases received from 20 Whole Slide Images (WSI). The results showed that the proposed approach achieves an F1 score equal to 0.94, showing great potential for the gastric glands’ classification.

Type: Proceedings paper
Title: Multi-scale Deformable Transformer for the Classification of Gastric Glands: The IMGL Dataset
Event: 1st International Workshop on Cancer Prevention Through Early Detection (CaPTion)
Location: Singapore, SINGAPORE
Dates: 22 Sep 2022
ISBN-13: 978-3-031-17978-5
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-031-17979-2_3
Publisher version: https://doi.org/10.1007/978-3-031-17979-2_3
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Computer Science, Computer Science, Interdisciplinary Applications, Gastric cancer, IMAGES, Intestinal metaplasia, Life Sciences & Biomedicine, Medical image classification, Oncology, Radiology, Nuclear Medicine & Medical Imaging, Science & Technology, Technology, Vision transformers
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Pathology
URI: https://discovery.ucl.ac.uk/id/eprint/10162873
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