A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images.

Background And Objectives: The detection of tumor-infiltrating lymphocytes (TILs) could aid in the development of objective measures of the infiltration grade and can support decision-making in breast cancer (BC). However, manual quantification of TILs in BC histopathological whole slide images (WSI) is currently based on a visual assessment, thus resulting not standardized, not reproducible, and time-consuming for pathologists. In this work, a novel pathomic approach, aimed to apply high-throughput image feature extraction techniques to analyze the microscopic patterns in WSI, is proposed.

The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study.

Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological resolution scales. Diffusion Weighted Imaging (DWI) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) are two functional MRI techniques that are commonly employed in clinic for the assessment of GBM tumor characteristics. This work presents initial results aiming at determining if radiomics features extracted from preoperative ADC maps and post-contrast T1 (T1C) images are associated with pathomic features arising from H&E digitized pathology images.

A survey on modern trainable activation functions.

In neural networks literature, there is a strong interest in identifying and defining activation functions which can improve neural network performance. In recent years there has been a renovated interest in the scientific community in investigating activation functions which can be trained during the learning process, usually referred to as trainable, learnable or adaptable activation functions. They appear to lead to better network performance.

Middle-Level Features for the Explanation of Classification Systems by Sparse Dictionary Methods.

Machine learning (ML) systems are affected by a pervasive lack of transparency. The eXplainable Artificial Intelligence (XAI) research area addresses this problem and the related issue of explaining the behavior of ML systems in terms that are understandable to human beings. In many explanation of XAI approaches, the output of ML systems are explained in terms of low-level features of their inputs.

Unsupervised entity and relation extraction from clinical records in Italian.

This paper proposes and discusses the use of text mining techniques for the extraction of information from clinical records written in Italian. However, as it is very difficult and expensive to obtain annotated material for languages different from English, we only consider unsupervised approaches, where no annotated training set is necessary. We therefore propose a complete system that is structured in two steps.