Spatial relation learning for explainable image classification and annotation in critical applications

Generation of Textual Explanations in XAI: the Case of Semantic Annotation

“…The fuzzy logic framework allows using words instead of numbers during computations and also during problem formalization. Indeed, relations are represented by a linguistic description that can be directly used in the explanation [7].…”

“…In [7], this approach was applied to organ annotation in medical images, with a focus on automatically generating the FCSP from few data. In the remainder of this paper, we will take this work as an illustration with an automatic generated FCSP.…”

“…Since CSP and FCSP are interpretable models and the process of solving is also interpretable and explainable, this kind of approaches are good candidates for explainable semantic annotation of images. Pierrard et al [7] propose algorithms to extract automatically relevant fuzzy spatial relations for image annotation from a few learning images whose regions are segmented and labelled. The appropriate relations are then used to constitute a FCSP for annotating areas of an image or a rule base to classify the image.…”

“…In this paper, we focus on the generation of a textual explanation of the semantic annotation in the context of [7]. Given a solution of such FCSPs and the degree of satisfaction of all the involved constraints, we propose and evaluate two algorithms to extract clues of the reasoning and to order the pieces of the explanation efficiently.…”

Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship

“…2 Artificial neural networks providing diagnostic, identification, and organizational potential, especially for large clinical and biological datasets, are becoming increasingly used in medical science. Drug discovery, [3][4][5][6][7][8][9][10] lead optimization 11 and synthesis, 12,13 cardiological and cardiovascular diseases, [14][15][16][17][18] medical image analysis, [19][20][21][22] diabetic diseases, 23,24 oncology research, 25,26 diagnosis, for example, alteration of oscillatory brain activity as a possible biomarker for use in Alzheimer's disease diagnosis, 27 are some of the examples of AI in service of medical science (Figure 1). Computer-aided drug design is not only an interesting concept but also a business requirement.…”