Publications

Due to the huge amount of available omic data, classifying samples according to various omics is a complex process. One of the most common approaches consists of creating a classifier for each omic and subsequently making a consensus among the classifiers that assign to each sample the most voted class among the outputs on the individual omics. However, this approach does not consider the confidence in the prediction ignoring that biological information coming from a certain omic may be more reliable than others. Therefore, it is here proposed a method consisting of a tree-based multi-layer perceptron (MLP), which estimates the class-membership probabilities for classification. In this way, it is not only possible to give relevance to all the omics, but also to label as Unknown those samples for which the classifier is uncertain in its prediction. The method was applied to a dataset composed of 909 kidney cancer samples for which these three omics were available: gene expression (mRNA), microRNA expression (miRNA), and methylation profiles (meth) data. The method is valid also for other tissues and on other omics (e.g. proteomics, copy number alterations data, single nucleotide polymorphism data). The accuracy and weighted average f1-score of the model are both higher than 95%. This tool can therefore be particularly useful in clinical practice, allowing physicians to focus on the most interesting and challenging samples.

Attention supports our urge to forage on social cues. Under certain circumstances, we spend the majority of time scrutinising people, markedly their eyes and faces, and spotting persons that are talking. To account for such behaviour, this article develops a computational model for the deployment of gaze within a multimodal landscape, namely a conversational scene. Gaze dynamics is derived in a principled way by reformulating attention deployment as a stochastic foraging problem. Model simulation experiments on a publicly available dataset of eye-tracked subjects are presented. Results show that the simulated scan paths exhibit similar trends of eye movements of human observers watching and listening to conversational clips in a free-viewing condition

Connected Components Labeling (CCL) is a crucial step of several image processing and computer vision pipelines. Many efficient sequential strategies exist, among which one of the most effective is the use of a block-based mask to drastically cut the number of memory accesses. In the last decade, aided by the fast development of Graphics Processing Units (GPUs), a lot of data parallel CCL algorithms have been proposed along with sequential ones. Applications that entirely run in GPU can benefit from parallel implementations of CCL that allow to avoid expensive memory transfers between host and device. In this paper, two new eight-connectivity CCL algorithms are proposed, namely Block-based Union Find (BUF) and Block-based Komura Equivalence (BKE). These algorithms optimize existing GPU solutions introducing a block-based approach. Extensions for three-dimensional datasets are also discussed. In order to produce a fair comparison with previously proposed alternatives, YACCLAB, a public CCL benchmarking framework, has been extended and made suitable for evaluating also GPU algorithms. Moreover, three-dimensional datasets have been added to its collection. Experimental results on real cases and synthetically generated datasets demonstrate the superiority of the new proposals with respect to state-of-the-art, both on 2D and 3D scenarios.

La procedura che rende un algoritmo più efficiente in termini di requisiti di memoria o tempo di esecuzione si chiama ottimizzazione e rappresenta un passaggio cruciale nell'elaborazione di immagini e video. È raro che il processo di ottimizzazione produca un algoritmo ottimo in senso assoluto, ma spesso occorre raggiungere un compromesso tra i requisiti di tempo e quelli di memoria. Ad ogni modo, esistono molti scenari in cui il tempo di esecuzione totale richiesto per completare un'attività è il vincolo più restrittivo. Gli algoritmi di elaborazione di immagini binarie, ad esempio, rappresentano un'operazione fondamentale nella maggior parte dei sistemi di analisi di immagini e video all'avanguardia, anche quando questi sono basati su tecniche di deep learning. Avere un'implementazione efficiente è quindi essenziale, specialmente quando questi sistemi devono essere impiegati in scenari con vincoli temporali, dove compromettere la qualità del risultato, o fare affidamento su hardware più performante, non è una strada percorribile. Questa tesi introduce ed esplora diversi approcci per l'ottimizzazione degli algoritmi di elaborazione di immagini binarie modellabili con tabelle decisionali. Esistono diversi problemi che possono essere definiti in questo modo: l’etichettatura delle componenti connesse, il thinning, il chain code e gli operatori morfologici sono alcuni di questi. In generale, tutti gli algoritmi in cui il valore di output per ciascun pixel dell'immagine è ottenuto dal valore del pixel stesso e di alcuni dei suoi vicini possono essere definiti utilizzando tabelle decisionali. Concentrandosi sull'etichettatura delle componenti connesse, vengono analizzati gli approcci all'avanguardia sia per ambienti sequenziali basati su CPU che per ambienti paralleli basati su CPU e GPU, focalizzandosi su come misurare in modo equo le prestazioni. Vengono quindi introdotti nuovi approcci per migliorare ulteriormente le prestazioni in termini di tempo totale di esecuzione, mostrando come queste tecniche possano essere generalizzate per migliorare qualsiasi algoritmo modellabile con tabelle decisionali. Infine, viene presentato un framework che consente di applicare automaticamente molte delle strategie di ottimizzazione precedentemente descritte ed analizzate ad un determinato algoritmo. Il framework, chiamato GRAPHGEN, prende come input una definizione del problema in termini di condizioni da verificare e azioni da eseguire ed è in grado di produrre come output il codice C/C++ che include tutte le ottimizzazioni necessarie. Rispetto agli approcci esistenti, gli algoritmi generati con GRAPHGEN hanno prestazioni significativamente migliori, sia su set di dati reali che su quelli sintetici.

Predicting the oncogenic potential of a gene fusion transcript is an important and challenging task in the study of cancer development. To this date, the available approaches mostly rely on protein domain analysis to provide a probability score explaining the oncogenic potential of a gene fusion. In this paper, a Convolutional Neural Network model is proposed to discriminate gene fusions into oncogenic or non-oncogenic, exploiting only the protein sequence without protein domain information. Our proposed model obtained accuracy value close to 90% on a dataset of fused sequences.

West Nile Disease (WND) is one of the most spread zoonosis in Italy and Europe caused by a vector-borne virus. Its transmission cycle is well understood, with birds acting as the primary hosts and mosquito vectors transmitting the virus to other birds, while humans and horses are occasional dead-end hosts. Identifying suitable environmental conditions across large areas containing multiple species of potential hosts and vectors can be difficult. The recent and massive availability of Earth Observation data and the continuous development of innovative Machine Learning methods can contribute to automatically identify patterns in big datasets and to make highly accurate identification of areas at risk. In this paper, we investigated the West Nile Virus (WNV) circulation in relation to Land Surface Temperature, Normalized Difference Vegetation Index and Surface Soil Moisture collected during the 160 days before the infection took place, with the aim of evaluating the predictive capacity of lagged remotely sensed variables in the identification of areas at risk for WNV circulation. WNV detection in mosquitoes, birds and horses in 2017, 2018 and 2019, has been collected from the National Information System for Animal Disease Notification. An Extreme Gradient Boosting model was trained with data from 2017 and 2018 and tested for the 2019 epidemic, predicting the spatio-temporal WNV circulation two weeks in advance with an overall accuracy of 0.84. This work lays the basis for a future early warning system that could alert public authorities when climatic and environmental conditions become favourable to the onset and spread of WNV.

In Continual Learning, a Neural Network is trained on a stream of data whose distribution shifts over time. Under these assumptions, it is especially challenging to improve on classes appearing later in the stream while remaining accurate on previous ones. This is due to the infamous problem of catastrophic forgetting, which causes a quick performance degradation when the classifier focuses on learning new categories. Recent literature proposed various approaches to tackle this issue, often resorting to very sophisticated techniques. In this work, we show that naïve rehearsal can be patched to achieve similar performance. We point out some shortcomings that restrain Experience Replay (ER) and propose five tricks to mitigate them. Experiments show that ER, thus enhanced, displays an accuracy gain of 51.2 and 26.9 percentage points on the CIFAR-10 and CIFAR-100 datasets respectively (memory buffer size 1000). As a result, it surpasses current state-of-the-art rehearsal-based methods.

To achieve robustness in Re-Identification, standard methods leverage tracking information in a Video-To-Video fashion. However, these solutions face a large drop in performance for single image queries (e.g., Image-To-Video setting). Recent works address this severe degradation by transferring temporal information from a Video-based network to an Image-based one. In this work, we devise a training strategy that allows the transfer of a superior knowledge, arising from a set of views depicting the target object. Our proposal - Views Knowledge Distillation (VKD) - pins this visual variety as a supervision signal within a teacher-student framework, where the teacher educates a student who observes fewer views. As a result, the student outperforms not only its teacher but also the current state-of-the-art in Image-To-Video by a wide margin (6.3% mAP on MARS, 8.6% on Duke-Video-ReId and 5% on VeRi-776). A thorough analysis - on Person, Vehicle and Animal Re-ID - investigates the properties of VKD from a qualitatively and quantitatively perspective.