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Related Experiment Video

Updated: Jan 22, 2026

Visualizing Lymph Node Structure and Cellular Localization using Ex-Vivo Confocal Microscopy

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Published on: August 9, 2019

Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases.

Iam Palatnik de Sousa¹, Marley Maria Bernardes Rebuzzi Vellasco², Eduardo Costa da Silva²

¹Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil. iam.palat@gmail.com.

Sensors (Basel, Switzerland)

|July 10, 2019

Summary

This study applies explainable artificial intelligence to understand how Convolutional Neural Networks (CNNs) detect tumor tissue in medical images. The findings suggest CNNs align with some human expert knowledge in pathology.

Keywords:

deep learning explainable AI lymph node metastases medical data

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Visualizing Lymph Node Structure and Cellular Localization using Ex-Vivo Confocal Microscopy

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Area of Science:

Medical Informatics
Artificial Intelligence in Medicine
Computational Pathology

Background:

Increasing demand for explainable AI models in healthcare.
Need for transparency in machine learning for medical applications.
Convolutional Neural Networks (CNNs) are powerful tools for analyzing medical images.

Purpose of the Study:

To explain how CNNs detect tumor tissue in histology whole slide images.
To apply the locally-interpretable model-agnostic explanations (LIME) methodology.
To analyze CNNs trained on the Patch Camelyon Benchmark.

Main Methods:

Utilized the locally-interpretable model-agnostic explanations (LIME) methodology.
Analyzed two publicly-available CNNs trained on the Patch Camelyon Benchmark.
Compared four superpixel generation algorithms, proposing a new parameter-free method.

Main Results:

Generated explanations for CNN tumor detection in histology patches.
Identified key patterns in true positive predictions.
Discussed characteristics of the generated explanations.

Conclusions:

CNN predictions demonstrate alignment with certain aspects of human expert knowledge.
The explainable AI approach provides insights into CNN decision-making.
Results suggest potential for AI in augmenting expert pathology analysis.