Stefano Marchesin

TL;DR: An optimisation of the Rijndael algorithm to speed up execution on 32-bits processors with memory constraints, such as those used in smart cards, is presented.

TL;DR: In this paper , the authors proposed and evaluated an approach to eliminate the need for manual annotations to train computer-aided diagnosis tools in digital pathology, which includes two components, to automatically extract semantically meaningful concepts from diagnostic reports and use them as weak labels to train convolutional neural networks (CNNs) for histopathology diagnosis.

TL;DR: In this paper, the Rijndael algorithm is used to convert data between an unencrypted and an encrypted format, including a plurality of rounds, each round is comprised of a fixed set of transformations applied to a two-dimensional array, designating states, of rows and columns of bit words.

TL;DR: This article proposed an unsupervised knowledge extraction system combining a rule-based expert system with pre-trained Machine Learning (ML) models, namely the Semantic Knowledge Extractor Tool (SKET).

TL;DR: TBGA as mentioned in this paper is a large-scale, semi-automatically annotated dataset for gene-disease association (GDAs) extraction, which contains more than 700k publications.