Papers by Francesco Calimeri
Using Heatmaps for Deep Learning based Disease Classification
2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2019
We present a novel framework for disease classification from high-dimensional gene expression dat... more We present a novel framework for disease classification from high-dimensional gene expression data or from several characteristic of patients. We take advantage of Principle Component Analysis to perform dimensionality reduction and heatmaps for embedding the complex information in a 2-D image, and we make use of a convolutional neural network to make classification of different tumor types. Experimental analyses show that the proposed method achieves good performance, and encourages its application to other genomic data or pathological context.
Fully automated approach of machine learning combined with deep learning to forecast the coronary artery disease in patients with non-alcoholic fatty liver disease
Journal of Hepatology, Jun 1, 2023
Proceedings 37th International Conference on Logic Programming (Technical Communications)
Electronic Proceedings in Theoretical Computer Science, 2021
Proceedings 38th International Conference on Logic Programming
Electronic proceedings in theoretical computer science, Aug 4, 2022
遺伝子発現プロファイリングによるびまん性大細胞型B細胞リンパ腫における分類と生存予測【JST・京大機械翻訳】
Lecture Notes in Computer Science, 2019
Practical Aspects of Declarative Languages - 20th International Symposium, {PADL} 2018, Los Angeles, CA, USA, January 8-9, 2018, Proceedings
AIM in Medical Informatics
Springer eBooks, 2022
Preface to the Special Issue from the 35th Italian Conference on Computational Logic (CILC 2020)
Journal of Logic and Computation, Jan 29, 2022
遺伝子発現と臨床データを用いた自動診断のためのデータ縮小とデータ可視化【JST・京大機械翻訳】
Artificial Intelligence in Medicine, 2020
遺伝子発現と臨床データを用いた自動診断のためのデータ縮小とデータ可視化【JST・京大機械翻訳】
Artificial Intelligence in Medicine, 2020
Using Heatmaps for Deep Learning based Disease Classification
We present a novel framework for disease classification from high-dimensional gene expression dat... more We present a novel framework for disease classification from high-dimensional gene expression data or from several characteristic of patients. We take advantage of Principle Component Analysis to perform dimensionality reduction and heatmaps for embedding the complex information in a 2-D image, and we make use of a convolutional neural network to make classification of different tumor types. Experimental analyses show that the proposed method achieves good performance, and encourages its application to other genomic data or pathological context.
Beyond rankings: Learning (more) from algorithm validation
Medical Image Analysis, May 1, 2023
The Stream Reasoning System I-DLV-sr: Enhancements and Applications in Smart Cities
Springer eBooks, 2022
AIM in Endoscopy Procedures
Springer eBooks, 2021
Frontiers in Robotics and AI
A tensor-based mutation operator for Neuroevolution of Augmenting Topologies (NEAT)
2017 IEEE Congress on Evolutionary Computation (CEC), 2017
In Genetic Algorithms, the mutation operator is used to maintain genetic diversity in the populat... more In Genetic Algorithms, the mutation operator is used to maintain genetic diversity in the population throughout the evolutionary process. Various kinds of mutation may occur over time, typically depending on a fixed probability value called mutation rate. In this work we make use of a novel data-science approach in order to adaptively generate mutation rates for each locus to the Neuroevolution of Augmenting Topologies (NEAT) algorithm. The trail of high quality candidate solutions obtained during the search process is represented as a third-order tensor; factorization of such a tensor reveals the latent relationship between solutions, determining the mutation probability which is likely to yield improvement at each locus. The single pole balancing problem is used as case study to analyze the effectiveness of the proposed approach. Results show that the tensor approach improves the performance of the standard NEAT algorithm for the case study.
Smart Devices and Large Scale Reasoning via ASP: Tools and Applications
Practical Aspects of Declarative Languages, 2022
The assessment of vascular complexity in the lower limbs provides relevant information about peri... more The assessment of vascular complexity in the lower limbs provides relevant information about peripheral artery diseases, with a relevant impact on both therapeutic decisions and on prognostic estimation. Such evaluation is currently carried out by human operators via visual inspection of cine-angiograms, resulting in conflicting results and scorings that are largely operator-dependent, mostly because of the technical difficulties in the quantification of vascular network and its flow capability. We propose a new method to automatically segment the vessel tree from cine-angiography video for intraoperative clinical evaluation, in order to improve the clinical interpretation of the complexity of vascular collaterals in Peripheral Arterial Occlusive Disease (PAOD) patients.
Prediction of Multiple Sclerosis Patient Disability from Structural Connectivity using Convolutional Neural Networks
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019
Prediction of disability progression in multiple sclerosis patients is a critical component of th... more Prediction of disability progression in multiple sclerosis patients is a critical component of their management. In particular, one challenge is to identify and characterize a patient profile who may benefit of efficient treatments. However, it is not yet clear whether a particular relation exists between the brain structure and the disability status.This work aims at producing a fully automatic model for the expanded disability status score estimation, given the brain structural connectivity representation of a multiple sclerosis patient. The task is addressed by first extracting the connectivity graph, obtained by combining brain grey matter parcellation and tractography extracted from Diffusion and T1-weighted Magnetic Resonance (MR) images, and then processing it via a convolutional neural network (CNN) in order to compute the predicted score. Experiments show that the herein proposed approach achieves promising results, thus resulting as an important step forward on the road to better predict the evolution of the disease.
Journal of Logic and Computation
Automated compliance checking, i.e. the task of automatically assessing whether states of affairs... more Automated compliance checking, i.e. the task of automatically assessing whether states of affairs comply with normative systems, has recently received a lot of attention from the scientific community, also as a consequence of the increasing investments in Artificial Intelligence technologies for the legal domain (LegalTech). The authors of this paper deem as crucial the research and implementation of compliance checkers that can directly process data in RDF format, as nowadays more and more (big) data in this format are becoming available worldwide, across a multitude of different domains. Among the automated technologies that have been used in recent literature, to the best of our knowledge, only two of them have been evaluated with input states of affairs encoded in RDF format. This paper formalizes a selected use case in these two technologies and compares the implementations, also in terms of simulations with respect to shared synthetic datasets.
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Papers by Francesco Calimeri