Abstract
Artificial Intelligence (AI) and Machine Learning (ML) are becoming some of the most dominant tools in scientific research. Despite this, little is often understood about the complex decisions taken by the models in predicting their results. This disproportionately affects biomedical and healthcare research where explainability of AI is one of the requirements for its wide adoption. To help answer the question of what the network is looking at when the labels do not correspond to the presence of objects in the image but the context in which they are found, we propose a novel framework for Explainable AI that combines and simultaneously analyses Class Activation and Segmentation Maps for thousands of images. We apply our approach to two distinct, complex examples of real-world biomedical research, and demonstrate how it can be used to provide a global and concise numerical measurement of how distinct classes of objects affect the final classification. We also show how this can be used to inform model selection, architecture design and aid traditional domain researchers in interpreting the model results.
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Provided by the Department of Information Technology at Universitá degli Studi di Milano, https://homes.di.unimi.it/scotti/all/.
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Acknowledgements
We wish to show our gratitude to Evotec for supporting this research, and specifically thank Michael Bodkin and Daniel Grindrod for providing useful insights and asking the right questions throughout the research, allowing this project to develop. We also thank Karsten Kottig for providing training masks for DO-U-Net for the Cell Painting Dataset, as well as sharing industry insight throughout the project.
We are also immensely grateful to the Department of Information Technology at Universitá degli Studi di Milano for providing the ALL_IDB1 dataset from the Acute Lymphoblastic Leukemia Image Database for Image Processing.
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Overton, T., Tucker, A., James, T., Hristozov, D. (2022). dunXai: DO-U-Net for Explainable (Multi-label) Image Classification. In: Bouadi, T., Fromont, E., Hüllermeier, E. (eds) Advances in Intelligent Data Analysis XX. IDA 2022. Lecture Notes in Computer Science, vol 13205. Springer, Cham. https://doi.org/10.1007/978-3-031-01333-1_17
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