New Data from University of Palermo Illuminate Findings in Engineering (Explainable Histopathology Image Classification With Self-organizing Maps: a Granular Computing Perspective).

A recent report from the University of Palermo in Italy discusses a new methodology for histopathological image classification in the field of engineering. The researchers propose a granular computing approach that combines deep learning neural networks with the explainability of self-organizing maps (SOM). This method allows for the visualization of a knowledge space that experts can use to analyze and classify new images, reducing false negatives and providing confidence in the classification results. The proposed system was tested using three histopathology image datasets and achieved accuracy comparable to state-of-the-art deep learning methods. This research introduces a novel and explainable method for medical image analysis, offering potential benefits for the field. [Extracted from the article]