Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge

Date

2024-12-01

Author

Ali, Sharib
Ghatwary, Noha
Jha, Debesh
Işık Polat, Ece
Polat, Gorkem
Yang, Chen
Li, Wuyang
Galdran, Adrian
Ballester, Miguel-Ángel González
Thambawita, Vajira
Hicks, Steven
Poudel, Sahadev
Lee, Sang-Woong
Jin, Ziyi
Gan, Tianyuan
Yu, ChengHui
Yan, JiangPeng
Yeo, Doyeob
Lee, Hyunseok
Tomar, Nikhil Kumar
Haithami, Mahmood
Ahmed, Amr
Riegler, Michael A.
Daul, Christian
Halvorsen, Pål
Rittscher, Jens
Salem, Osama E.
Lamarque, Dominique
Cannizzaro, Renato
Realdon, Stefano
de Lange, Thomas
East, James E.

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Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.

URI

https://hdl.handle.net/11511/108730

Collections

Graduate School of Informatics, Article