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Cited by 26Radiomics and Machine Learning in Medical ImagingAI in cancer detectionExplainable Artificial Intelligence (XAI)

SISC: End-to-End Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells

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Kumar, Devinder, Sankar, Vignesh, Clausi, David, Taylor, Graham W., Wong, Alexander·2019·IEEE Access
DOI 10.1109/access.2019.2945524
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Abstract

Lung cancer is the leading cause of cancer-related death worldwide. Computer-aided diagnosis (CAD) systems have shown significant promise in recent years for facilitating the effective detection and classification of abnormal lung nodules in computed tomography (CT) scans. While hand-engineered radiomic features have been traditionally used for lung cancer prediction, there have been significant recent successes achieving state-of-the-art results in the area of discovery radiomics. Here, radiomic sequencers comprising of highly discriminative radiomic features are discovered directly from archival medical data. However, the interpretation of predictions made using such radiomic sequencers remains a challenge. A novel end-to-end interpretable discovery radiomics-driven lung cancer prediction pipeline has been designed, build, and tested. The radiomic sequencer being discovered possesses a deep architecture comprised of stacked interpretable sequencing cells (SISC). The SISC architecture is shown to outperform previous approaches while providing more insight in to its decision making process. The SISC radiomic sequencer is able to achieve state-of-the-art results in lung cancer prediction, and also offers prediction interpretability in the form of critical response maps. The critical response maps are useful for not only validating the predictions of the proposed SISC radiomic sequencer, but also provide improved radiologist-machine collaboration for effective diagnosis.

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Cited by

Sorted by citations

Authors
Kumar, D., Sankar, V., Clausi, D., Taylor, G. W., & Wong, A.
Affiliations
  • University of Waterloo (CA)
  • University of Guelph (CA)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019
Volume
7
Pages
145444-145454
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2019.2945524
OpenAlex
W2978236883
Cited by
26
References
14
Citations by year
2026: 2 · 2025: 7 · 2024: 2 · 2023: 3 · 2022: 4 · 2021: 4 · 2020: 4
FWCI
2.00
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Access (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Nvidia
  • Canada Research Chairs
  • Natural Sciences and Engineering Research Council of Canada
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Keywords
lung cancer prediction, model interpretability, computer-aided diagnosis, pulmonary nodules, computed tomography

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