Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns.
Deep learning techniques offer improvements in computer-aided diagnosis systems. However, acquiring image domain annotations is challenging due to the knowledge and commitment required of expert pathologists. Pathologists often identify regions in whole slide images with diagnostic relevance rather...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 439 - 455 |
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| Main Authors: | , , , , , , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Feb2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184471489&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184471489 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2025 vid: 38 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184471489 184471489 184471489 10.1007/s10278-024-01202-x 184471489 ppf: 439 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Robust ROI Detection in Whole Slide Images Guided by Pathologists' Viewing Patterns. aug: au: Ghezloo, Fatemeh Chang, Oliver H. Knezevich, Stevan R. Shaw, Kristin C. Thigpen, Kia Gianni Reisch, Lisa M. Shapiro, Linda G. Elmore, Joann G. affil: https://ror.org/00cvxb145 Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA sug: subj: Deep Learning Diagnosis, Computer Assisted Methods Image Interpretation, Computer Assisted Image Processing, Computer Assisted Melanoma Diagnosis Melanoma Pathology Skin Neoplasms Radiography Pathologists Diagnosis, Laboratory Methods Slides Human Funding Source Male Female Skin Biopsy Histological Techniques Staining and Labeling Melanoma Radiography Descriptive Statistics Sensitivity and Specificity Data Analysis Software Male Female ab: Deep learning techniques offer improvements in computer-aided diagnosis systems. However, acquiring image domain annotations is challenging due to the knowledge and commitment required of expert pathologists. Pathologists often identify regions in whole slide images with diagnostic relevance rather than examining the entire slide, with a positive correlation between the time spent on these critical image regions and diagnostic accuracy. In this paper, a heatmap is generated to represent pathologists' viewing patterns during diagnosis and used to guide a deep learning architecture during training. The proposed system outperforms traditional approaches based on color and texture image characteristics, integrating pathologists' domain expertise to enhance region of interest detection without needing individual case annotations. Evaluating our best model, a U-Net model with a pre-trained ResNet-18 encoder, on a skin biopsy whole slide image dataset for melanoma diagnosis, shows its potential in detecting regions of interest, surpassing conventional methods with an increase of 20%, 11%, 22%, and 12% in precision, recall, F1-score, and Intersection over Union, respectively. In a clinical evaluation, three dermatopathologists agreed on the model's effectiveness in replicating pathologists' diagnostic viewing behavior and accurately identifying critical regions. Finally, our study demonstrates that incorporating heatmaps as supplementary signals can enhance the performance of computer-aided diagnosis systems. Without the availability of eye tracking data, identifying precise focus areas is challenging, but our approach shows promise in assisting pathologists in improving diagnostic accuracy and efficiency, streamlining annotation processes, and aiding the training of new pathologists. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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