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SAMCNet: Towards a Spatially Explainable AI Approach for Classifying MxIF Oncology Data

Published: 14 August 2022 Publication History

Abstract

The goal of spatially explainable artificial intelligence (AI) classification approach is to build a classifier to distinguish two classes (e.g., responder, non-responder) based on the their spatial arrangements (e.g., spatial interactions between different point categories) given multi-category point data from two classes. This problem is important for generating hypotheses towards discovering new immunotherapies for cancer treatment as well as for other applications in biomedical research and microbial ecology. This problem is challenging due to an exponential number of category subsets which may vary in the strength of their spatial interactions. Most prior efforts on using human selected spatial association measures may not be sufficient for capturing the relevant spatial interactions (e.g., surrounded by) which may be of biological significance. In addition, the related deep neural networks are limited to category pairs and do not explore larger subsets of point categories. To overcome these limitations, we propose a Spatial-interaction Aware Multi-Category deep neural Network (SAMCNet) architecture and contribute novel local reference frame characterization and point pair prioritization layers for spatially explainable classification. Experimental results on multiple cancer datasets (e.g., MxIF) show that the proposed architecture provides higher prediction accuracy over baseline methods. A real-world case study demonstrates that the proposed work discovers patterns that are missed by the existing methods and has the potential to inspire new scientific discoveries.

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  • (2024)Spatial Computing Opportunities in Biomedical Decision Support: The Atlas-EHR VisionACM Transactions on Spatial Algorithms and Systems10.1145/367920110:3(1-36)Online publication date: 23-Jul-2024
  • (2024)XAI Unveiled: Revealing the Potential of Explainable AI in Medicine - A Systematic ReviewIEEE Access10.1109/ACCESS.2024.3514197(1-1)Online publication date: 2024
  • (2023)Survey on Explainable AI: From Approaches, Limitations and Applications AspectsHuman-Centric Intelligent Systems10.1007/s44230-023-00038-y3:3(161-188)Online publication date: 10-Aug-2023
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    cover image ACM Conferences
    KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
    August 2022
    5033 pages
    ISBN:9781450393850
    DOI:10.1145/3534678
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 14 August 2022

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    Author Tags

    1. mxif
    2. oncology
    3. spatial interactions
    4. spatially explainable classifier

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    View all
    • (2024)Spatial Computing Opportunities in Biomedical Decision Support: The Atlas-EHR VisionACM Transactions on Spatial Algorithms and Systems10.1145/367920110:3(1-36)Online publication date: 23-Jul-2024
    • (2024)XAI Unveiled: Revealing the Potential of Explainable AI in Medicine - A Systematic ReviewIEEE Access10.1109/ACCESS.2024.3514197(1-1)Online publication date: 2024
    • (2023)Survey on Explainable AI: From Approaches, Limitations and Applications AspectsHuman-Centric Intelligent Systems10.1007/s44230-023-00038-y3:3(161-188)Online publication date: 10-Aug-2023
    • (2022)CSCDProceedings of the 10th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data10.1145/3557917.3567619(36-46)Online publication date: 1-Nov-2022
    • (2022)Mining taxonomy-aware colocationsProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3561034(1-11)Online publication date: 1-Nov-2022

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