Evolutionary deep feature selection for compact representation of gigapixel images in digital pathology.

Autor: Bidgoli AA; NICI Lab(1), Ontario Tech University, Oshawa, Canada., Rahnamayan S; NICI Lab(2), Brock University, St. Catharines, Canada; Kimia Lab(3), University of Waterloo, Waterloo, Canada. Electronic address: shahryar.rahnamayan@uoit.ca., Dehkharghanian T; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada., Riasatian A; Kimia Lab(3), University of Waterloo, Waterloo, Canada., Kalra S; Kimia Lab(3), University of Waterloo, Waterloo, Canada., Zaveri M; Kimia Lab(3), University of Waterloo, Waterloo, Canada., Campbell CJV; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada., Parwani A; Department of Pathology, Wexner Medical Center, Ohio State University, Columbus, USA., Pantanowitz L; Department of Pathology, University of Michigan, Ann Arbor, USA., Tizhoosh HR; Kimia Lab(3), University of Waterloo, Waterloo, Canada; Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.
Jazyk: angličtina
Zdroj: Artificial intelligence in medicine [Artif Intell Med] 2022 Oct; Vol. 132, pp. 102368. Date of Electronic Publication: 2022 Jul 25.
DOI: 10.1016/j.artmed.2022.102368
Abstrakt: Despite the recent progress in Deep Neural Networks (DNNs) to characterize histopathology images, compactly representing a gigapixel whole-slide image (WSI) via salient features to enable computational pathology is still an urgent need and a significant challenge. In this paper, we propose a novel WSI characterization approach to represent, search and classify biopsy specimens using a compact feature vector (CFV) extracted from a multitude of deep feature vectors. Since the non-optimal design and training of deep networks may result in many irrelevant and redundant features and also cause computational bottlenecks, we proposed a low-cost stochastic method to optimize the output of pre-trained deep networks using evolutionary algorithms to generate a very small set of features to accurately represent each tissue/biopsy. The performance of the proposed method has been assessed using WSIs from the publicly available TCGA image data. In addition to acquiring a very compact representation (i.e., 11,000 times smaller than the initial set of features), the optimized features achieved 93% classification accuracy resulting in 11% improvement compared to the published benchmarks. The experimental results reveal that the proposed method can reliably select salient features of the biopsy sample. Furthermore, the proposed approach holds the potential to immensely facilitate the adoption of digital pathology by enabling a new generation of WSI representation for efficient storage and more user-friendly visualization.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2022 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: