A machine learning approach for the recognition of melanoma skin cancer on macroscopic images

In the last years, computer vision systems for the detection of skin cancer have being proposed, specially using machine learning techniques for the classification of the disease and features based on the ABCD dermatology criterion, which gives information on the status of the skin lesion based on static properties such as geometry, color and texture, making it an appropriate criterion for medical diagnosis systems that work through images. This paper proposes a novel skin cancer classification system that works on images taken from a standard camera and studies the impact on the results of the smoothed bootstrapping, which was used to augment the original dataset. Eight classifiers with different topologies (KNN, ANN and SVM) were compared, with and without data augmentation, showing that the classifier with the highest performance as well as the must balanced one was the ANN with data augmentation, achieving an AUC of 87.1%, which saw an improvement from an AUC of 84.3% of the ANN trained with the original dataset

Applications of pattern classification to time-domain signals

Many different kinds of physics are used in sensors that produce time-domain signals, such as ultrasonics, acoustics, seismology, and electromagnetics. The waveforms generated by these sensors are used to measure events or detect flaws in applications ranging from industrial to medical and defense-related domains. Interpreting the signals is challenging because of the complicated physics of the interaction of the fields with the materials and structures under study. often the method of interpreting the signal varies by the application, but automatic detection of events in signals is always useful in order to attain results quickly with less human error. One method of automatic interpretation of data is pattern classification, which is a statistical method that assigns predicted labels to raw data associated with known categories. In this work, we use pattern classification techniques to aid automatic detection of events in signals using features extracted by a particular application of the wavelet transform, the Dynamic Wavelet Fingerprint (DWFP), as well as features selected through physical interpretation of the individual applications. The wavelet feature extraction method is general for any time-domain signal, and the classification results can be improved by features drawn for the particular domain. The success of this technique is demonstrated through four applications: the development of an ultrasonographic periodontal probe, the identification of flaw type in Lamb wave tomographic scans of an aluminum pipe, prediction of roof falls in a limestone mine, and automatic identification of individual Radio Frequency Identification (RFID) tags regardless of its programmed code. The method has been shown to achieve high accuracy, sometimes as high as 98%

CGM based basal-insulin titration in insulin-naïve type 2 diabetic subjects: an in-silico study

openLa fisiopatologia del diabete di tipo 2 (T2D) consiste in un malfunzionamento dei circuiti di feedback che coinvolgono la secrezione di insulina (disfunzione delle cellule β) e/o l'azione dell'insulina (stato di resistenza insulinica), il quale malfunzionamento porta il soggetto in uno stato di iperglicemia (livello di glucosio nel sangue elevato). Con la progressione della malattia (T2D in stato avanzato), i soggetti possono richiedere la somministrazione di insulina esogena per controllare la glicemia: l’azione combinata di insuline ad azione rapida per il controllo durante i pasti ed insuline ad azione prolungata per il controllo della concentrazione glicemica pre-prandiale e postprandiale. L’attuale procedura per determinare la dose ottimale di insulina basale in soggetti che non hanno mai utilizzato in precedenza l’insulina per il trattamento del diabete (naïve all’insulina) consiste in una regola di titolazione non personalizzata. Secondo le linee guida ADA, i soggetti devono iniziare la titolazione con una dose di insulina bassa che viene fatta variare seguendo incrementi e decrementi predefiniti: tali variazioni sono basate sull’automonitoraggio (SMBG) a digiuno della glicemia; lo scopo è di raggiungere un livello target di glicemia a digiuno (FPG). Lo scopo di questa tesi è sviluppare una regola di titolazione dell'insulina basale personalizzata basata sui bisogni specifici di insulina dei soggetti. In particolare, è stato utilizzato il monitoraggio continuo del glucosio (CGM) e delle relative metriche temporali ricavate da tale segnale, le quali sono utilizzate dai medici per valutare la qualità del controllo glicemico: il tempo speso al di sopra dell'intervallo glicemico target (TAR), tempo speso nell'intervallo glicemico target (TIR) e tempo sotto l'intervallo glicemico target (TBR). La popolazione utilizzata è composta da 300 soggetti virtuali, ai quali è stata somministrata lo stato dell’arte delle regole per la titolazione dell’insulina basale (DUAL I). I soggetti sono stati quindi classificati utilizzando un modello di regressione logistica precedentemente addestrato. In particolare, sono stati divisi in base alla loro dose finale di insulina tra alto fabbisogno insulinico (HIN) e basso fabbisogno insulinico (LIN). I soggetti classificati come HIN sono stati titolati utilizzando i quattro nuovi algoritmi. Le metriche temporali del segnale GCM ottenute dalle nuove regole di titolazione sono state confrontate con quelle ottenute utilizzando DUAL I. Tra i nuovi algoritmi testati il migliore risulta essere la quarta versione. Tale risultato è il prodotto di una selezione che ha considerato la correlazione tra la dose finale di insulina somministrata da DUAL I e quella di ciascun algoritmo di titolazione (per il quarto algoritmo: ρ= 0,82, pvalue<10-8). L’applicazione del nuovo algoritmo ha mostrato un aumento statisticamente e clinicamente significativo del TIR, nonché una diminuzione significativa del TAR accompagnata da una riduzione significativa del FPG. Lo svantaggio principale è stato un aumento statisticamente significativo della TBR fino al terzo mese; tuttavia, dopo questo periodo questa differenza non è risultata più significativa. Nonostante i buoni risultati complessivamente ottenuti, potrebbero essere apportati miglioramenti in futuro. Nella fattispecie, si possono considerare gli altri trend per aggiungere informazioni significative che migliorino il processo decisionale. Inoltre, si potrebbero condurre altri studi su come regolare l’aggressività degli algoritmi oggetto di questa tesi.The pathophysiology of the type 2 diabetes (T2D) consists in a malfunctioning of the feedback loops between insulin secretion (β-cell disfunction) and/or insulin action (insulin resistance state) leading to an abnormally high blood glucose level. With the progression of the disease (advance stage T2D), subjects may need exogenous insulin to control their glycaemia, using fast acting insulins during meals and/or long-acting insulins, to control fasting (pre-breakfast) and postprandial glucose concentration. The current procedure to determine the optimal basal insulin dose in subjects who have never previously used insulin to treat diabetes (insulin-naïve) consists in a non-personalized titration rule. According to ADA guidelines, subjects must start the titration with a low insulin dose that is progressively adjusted, following predefined increments/decrements, based only on self-monitoring blood glucose (SMBG) pre-breakfast measurements (Gpre), to reach a target fasting glucose level. The aim of this thesis is to develop a more personalized basal insulin titration rule based on subjects’ specific insulin needs, continuous glucose monitoring (CGM) and common CGM metrics used by clinician to assess the quality of glucose control i.e., time above range (TAR), time in range (TIR), and time below range (Tb). We used a dataset consisting of 300 in silico subjects who underwent a literature titration rule (DUAL I). Subjects were then classified as high insulin needs (HIN) and low insulin needs (LIN), based on their final insulin dose, using a literature logistic regression model. The classified HIN subjects underwent four new rules and their GCM time metrics were compared with the ones obtained using DUAL I. Among the new tested rules, the best one, which is selected in terms of higher correlation with DUAL I final insulin dose (ρ=0.85, p-value<10-8), showed a statistically and clinically significant increase of TIR, as well as a significant decrease of TAR accompanied by a significant reduction in the GPre. The main drawback was a statistically significant increase in the Tb until the third month, anyway after this period this difference was not significant anymore. Despite the overall good results achieved, improvements could be made in the future, looking if other trends can add significant features which improve the decision process, but also making studies on how to tune the aggressiveness of the rules object of this thesis

Bootstrapping Non-Parallel Voice Conversion From Speaker-Adaptive Text-to-Speech

Voice conversion (VC) and text-to-speech (TTS) are two tasks that share a similar objective, generating speech with a target voice. However, they are usually developed independently under vastly different frameworks. In this paper, we propose a methodology to bootstrap a VC system from a pretrained speaker-adaptive TTS model and unify the techniques as well as the interpretations of these two tasks. Moreover by offloading the heavy data demand to the training stage of the TTS model, our VC system can be built using a small amount of target speaker speech data. It also opens up the possibility of using speech in a foreign unseen language to build the system. Our subjective evaluations show that the proposed framework is able to not only achieve competitive performance in the standard intra-language scenario but also adapt and convert using speech utterances in an unseen language.Comment: Accepted for IEEE ASRU 201

Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project

We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function

Sequential decision making in artificial musical intelligence

Over the past 60 years, artificial intelligence has grown from a largely academic field of research to a ubiquitous array of tools and approaches used in everyday technology. Despite its many recent successes and growing prevalence, certain meaningful facets of computational intelligence have not been as thoroughly explored. Such additional facets cover a wide array of complex mental tasks which humans carry out easily, yet are difficult for computers to mimic. A prime example of a domain in which human intelligence thrives, but machine understanding is still fairly limited, is music. Over the last decade, many researchers have applied computational tools to carry out tasks such as genre identification, music summarization, music database querying, and melodic segmentation. While these are all useful algorithmic solutions, we are still a long way from constructing complete music agents, able to mimic (at least partially) the complexity with which humans approach music. One key aspect which hasn't been sufficiently studied is that of sequential decision making in musical intelligence. This thesis strives to answer the following question: Can a sequential decision making perspective guide us in the creation of better music agents, and social agents in general? And if so, how? More specifically, this thesis focuses on two aspects of musical intelligence: music recommendation and human-agent (and more generally agent-agent) interaction in the context of music. The key contributions of this thesis are the design of better music playlist recommendation algorithms; the design of algorithms for tracking user preferences over time; new approaches for modeling people's behavior in situations that involve music; and the design of agents capable of meaningful interaction with humans and other agents in a setting where music plays a roll (either directly or indirectly). Though motivated primarily by music-related tasks, and focusing largely on people's musical preferences, this thesis also establishes that insights from music-specific case studies can also be applicable in other concrete social domains, such as different types of content recommendation. Showing the generality of insights from musical data in other contexts serves as evidence for the utility of music domains as testbeds for the development of general artificial intelligence techniques. Ultimately, this thesis demonstrates the overall usefulness of taking a sequential decision making approach in settings previously unexplored from this perspectiveComputer Science

Learning with Minimal Supervision: New Meta-Learning and Reinforcement Learning Algorithms

Standard machine learning approaches thrive on learning from huge amounts of labeled training data, but what if we don’t have access to large amounts of labeled datasets? Humans have a remarkable ability to learn from only a few examples. To do so, they either build upon their prior learning experiences, or adapt to new circumstances by observing sparse learning signals. In this dissertation, we promote algorithms that learn with minimal amounts of supervision inspired by these two ideas. We discuss two families for minimally supervised learning algorithms based on meta-learning (or learning to learn) and reinforcement learning approaches.In the first part of the dissertation, we discuss meta-learning approaches for learning with minimal supervision. We present three meta-learning algorithms for few-shot adaptation of neural machine translation systems, promoting fairness in learned models by learning to actively learn under fairness parity constraints, and learning better exploration policies in the interactive contextual bandit setting. All of these algorithms simulate settings in which the agent has access to only a few labeled samples. Based on these simulations, the agent learns how to solve future learning tasks with minimal supervision. In the second part of the dissertation, we present learning algorithms based on reinforcement and imitation learning. In many settings the learning agent doesn’t have access to fully supervised training data, however, it might be able to leverage access to a sparse reward signal, or an expert that can be queried to collect the labeled data. It is important then to utilize these learning signals efficiently. Towards achieving this goal, we present three learning algorithms for learning from very sparse reward signals, leveraging access to noisy guidance, and solving structured prediction learning tasks under bandit feedback. In all cases, the result is a minimally supervised learning algorithm that can effectively learn given access to sparse reward signals

Identifying the molecular components that matter: a statistical modelling approach to linking functional genomics data to cell physiology

Functional genomics technologies, in which thousands of mRNAs, proteins, or metabolites can be measured in single experiments, have contributed to reshape biological investigations. One of the most important issues in the analysis of the generated large datasets is the selection of relatively small sub-sets of variables that are predictive of the physiological state of a cell or tissue. In this thesis, a truly multivariate variable selection framework using diverse functional genomics data has been developed, characterized, and tested. This framework has also been used to prove that it is possible to predict the physiological state of the tumour from the molecular state of adjacent normal cells. This allows us to identify novel genes involved in cell to cell communication. Then, using a network inference technique networks representing cell-cell communication in prostate cancer have been inferred. The analysis of these networks has revealed interesting properties that suggests a crucial role of directional signals in controlling the interplay between normal and tumour cell to cell communication. Experimental verification performed in our laboratory has provided evidence that one of the identified genes could be a novel tumour suppressor gene. In conclusion, the findings and methods reported in this thesis have contributed to further understanding of cell to cell interaction and multivariate variable selection not only by applying and extending previous work, but also by proposing novel approaches that can be applied to any functional genomics data

Discriminative Appearance Models for Face Alignment

The proposed face alignment algorithm uses local gradient features as the appearance representation. These features are obtained by pixel value comparison, which provide robustness against changes in illumination, as well as partial occlusion and local deformation due to the locality. The adopted features are modeled in three discriminative methods, which correspond to different alignment cost functions. The discriminative appearance modeling alleviate the generalization problem to some extent