Local interpretable model-agnostic explanations for classification of lymph node metastases
Open accessAbstract
An application of explainable artificial intelligence on medical data is presented. There is an increasing demand in machine learning literature for such explainable models in health-related applications. This work aims to generate explanations on how a Convolutional Neural Network (CNN) detects tumor tissue in patches extracted from histology whole slide images. This is achieved using the "locally-interpretable model-agnostic explanations" methodology. Two publicly-available convolutional neural networks trained on the Patch Camelyon Benchmark are analyzed. Three common segmentation algorithms are compared for superpixel generation, and a fourth simpler parameter-free segmentation algorithm is proposed. The main characteristics of the explanations are discussed, as well as the key patterns identified in true positive predictions. The results are compared to medical annotations and literature and suggest that the CNN predictions follow at least some aspects of human expert knowledge.
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Intraoperative urine output, IV fluid infusion, blood product transfusion, and dynamic changes of hemodynamic features are significant causes of cardiac surgery-associated acute kidney injury.
“Using SHAP values they described that the intraoperative urine output, IV fluid infusion, blood product transfusion, and dynamic changes of hemodynamic features are significant causes of injury.”
From Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2022) · cites this paper as [113]
These factors were not revealed using traditional techniques.
“They also stated that these factors were not revealed using traditional techniques.”
From Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2022) · cites this paper as [113]
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- Authors
- Palatnik de Sousa, I., Maria Bernardes Rebuzzi Vellasco, M., & Costa da Silva, E.
- Affiliations
- Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-07-05
- Volume
- 19
- Issue
- 13
- Pages
- 2969
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC6651753
- OpenAlex
- W2955748575
- Cited by
- 155
- References
- 13
- Citations by year
- 2026: 17 · 2025: 18 · 2024: 36 · 2023: 31 · 2022: 28 · 2021: 16 · 2020: 9
- FWCI
- 6.04
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (165019/2018-2)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- explainable AI, lime, lymph node metastasis, histopathology, whole slide images, convolutional neural networks, superpixel segmentation, tumor detection, medical image classification, human expert knowledge
- MeSH terms
- Deep Learning, Algorithms, Humans, Image Processing, Computer-Assisted, Lymph Nodes, Lymphatic Metastasis, Models, Biological, Neural Networks, Computer
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