This work aimed to investigate whether automated classifiers belonging to feature-based and deep learning may approach brain metastases segmentation successfully. Support Vector Machine and V-Net Convolutional Neural Network are selected as representatives of the two approaches. In the experiments, we consider several configurations of the two methods to segment brain metastases on contrast-enhanced T1-weighted magnetic resonance images. Performances were evaluated and compared under critical conditions imposed by the clinical radiotherapy domain, using in-house dataset and public dataset created for the Multimodal Brain Tumour Image Segmentation (BraTS) challenge. Our results showed that the feature-based and the deep network approaches are promising for the segmentation of Magnetic Resonance Imaging (MRI) brain metastases achieving both an acceptable level of performance. Experimental results also highlight different behaviour between the two methods. Support vector machine (SVM) improves performance with a smaller training set, but it is unable to manage a high level of heterogeneity in the data and requires post-processing refinement stages. The V-Net model shows good performances when trained on multiple heterogeneous cases but requires data augmentations and transfer learning procedures to optimise its behaviour. The paper illustrates a software package implementing an integrated set of procedures for active support in segmenting brain metastases within the radiotherapy workflow.

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During decades, Natural language processing (NLP) expanded its range of tasks, from document classification to automatic text summarization, sentiment analysis, text mining, machine translation, automatic question answering and others. In 2018, T. Young described NLP as a theory-motivated range of computational techniques for the automatic analysis and representation of human language. Outside and before AI, human language has been studied by specialists from various disciplines: linguistics, philosophy, logic, psychology. The aim of this work is to build a neural network to perform a sentiment analysis on Italian reviews from the chatbot customer service. Sentiment analysis is a data mining process which identifies and extracts subjective information from text. It could help to understand the social sentiment of clients, respect a business product or service. It could be a simple classification task that analyses a sentence and tells whether the underlying sentiment is positive or negative. The potentiality of deep learning techniques made this simple classification task evolve, creating new, more complex sentiment analysis, e.g. Intent Analysis and Contextual Semantic Search.