Autosomal dominant polycystic kidney disease (ADPKD) is one of the most widespread genetic disorders affecting the kidney. Nevertheless, there is still no cure for ADPKD. Domain experts test the effectiveness of different treatments by investigating how they can reduce the number and dimension of cysts on kidney tissues. Image processing of the microscope acquisitions is then an expensive but necessary operation currently performed by operators to determine and compare cyst size and quantity. In this work, we propose a deep learning algorithm for fast and accurate cysts detection in sequential 2-D images. Experiments on 507 RGB immunofluorescence images of 8 kidney tubules show that the proposed U-Net-based deep-learning solution can automatically segment images with increasing performance at larger cyst dimensions (Pr > 0.8, Re > 0.75 for cysts larger than 32 µm2). Such a reliable method performing an accurate cyst segmentation can be a valid support for researchers in optimising the effort to find new effective treatments for ADPKD.

Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether artificial intelligence working with chest X-ray (CXR) scans and clinical data can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. Indeed, further to induce lower radiation dose than computed tomography (CT), CXR is a simpler and faster radiological technique, being also more widespread. In this respect, we present three approaches that use features extracted from CXR images, either handcrafted or automatically learnt by convolutional neuronal networks, which are then integrated with the clinical data. As a further contribution, this work introduces a repository that collects data from 820 patients enrolled in six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, suggesting that clinical data and images have the potential to provide useful information for the management of patients and hospital resources.

In this paper, we tackle the task of automatically analyzing 3D volumetric scans obtained from computed tomography (CT) devices. In particular, we address a particular task for which data is very limited: the segmentation of ancient Egyptian mummies CT scans. We aim at digitally unwrapping the mummy and identify different segments such as body, bandages and jewelry. The problem is complex because of the lack of annotated data for the different semantic regions to segment, thus discouraging the use of strongly supervised approaches. We, therefore, propose a weakly supervised and efficient interactive segmentation method to solve this challenging problem. After segmenting the wrapped mummy from its exterior region using histogram analysis and template matching, we first design a voxel distance measure to find an approximate solution for the body and bandage segments. Here, we use geodesic distances since voxel features as well as spatial relationship among voxels is incorporated in this measure. Next, we refine the solution using a GrabCut based segmentation together with a tracking method on the slices of the scan that assigns labels to different regions in the volume, using limited supervision in the form of scribbles drawn by the user. The efficiency of the proposed method is demonstrated using visualizations and validated through quantitative measures and qualitative unwrapping of the mummy.

An important task in brain connectivity research is the classification of patients from healthy subjects. In this work, we present a two-step mathematical framework allowing to discriminate between two groups of people with an application to multiple sclerosis. The proposed approach exploits the properties of the connectivity matrices determined using the covariances between signals of a fixed set of brain areas. These positive semidefinite matrices lay on a Riemannian manifold, allowing to use a geodesic distance defined on this space. In order to generate a vector representation useful for classification purposes, but still preserving the network structure, we encoded the data exploiting the network attractors determined by a geodesic clustering of connectivity matrices. Then clustering centroids were used as a dictionary allowing to encode subject's connectivity matrices as a vector of geodesic distances. A Linear Support Vector Machine was then used to perform classification between subjects. To demonstrate the advantage of using geodesic metrics in this framework, we conducted the same analysis using Euclidean metric. Experimental results validate the fact that employing geodesic metric in this framework leads to a higher classification performance, whereas performance with a Euclidean metric was sub-optimal.

Functional Magnetic Resonance Imaging (fMRI) is a commonly used technique to evaluate brain activity, and can be used to distinguish patients from healthy controls in a variety of diseases. In this work, we present a two-step approach to discriminate healthy subjects against those affected by either Autism Spectrum Disorder or Schizophrenia on the basis of their connectivity patterns. We exploited the property that connectivity patterns described by positive definite matrices define a Riemannian manifold. In this framework, to generate a vector representation used in the classification task, we performed a geodesic clustering of the connectivity matrices. Cluster centroids were then used as a dictionary allowing to encode all subjects graphs as vectors of geodesic distances. A linear Support Vector Machine was then used to classify subjects. To show the advantage of using geodesic distances for this problem, the same analysis was conducted using a Euclidean metric. Experiments show that employing Euclidean distances leads to a lower classification performance and possibly to the definition of the wrong number of clusters, whereas geodesic clustering results in a significantly improved accuracy.

Exploring the human structural connectome often involves dealing with millions of white matter tracts reconstructed from diffusion MRI. Reducing the dimensionality of such data by grouping tracts into bundles can prove essential for subsequent analyses. Many unsupervised clustering algorithms aim at providing such bundles but often require the choice of a distance metric and suffer from memory storage issues relating to the size of the datasets. We propose for the first time a neural network approach for the unsupervised clustering of white matter tracts. It has the main properties of learning automatically the tract features and scaling well with the data size. As a proof of concept, we compare both quantitatively and qualitatively the computed tract clusters with a commonly used clustering method. The proposed approach shows results similar to the reference approach while not using any distance matrix or similarity metric.

Mouse models play an important role in preclinical research and drug discovery for human diseases. The fact that mice are a social species partaking in social interactions of high degree facilitates the study of diseases characterized by social alterations. Hence, robust animal tracking is of great importance in order to build tools capable of automatically analyzing social behavioral interactions of multiple mice. However, the presence of occlusions is a major problem in multiple mice tracking. To deal with this problem, we present here a tracking algorithm based on Kalman filter and Gaussian Mixture Modeling. Specifically, Kalman tracking is used to track the mice and when occlusions happen, we fit 2D Gaussian distributions to separate mouse blobs. This helps us to prevent mice identity swaps as it is an important feature for accurate behavior analysis. As the results of our experiments show, the proposed algorithm results in much fewer identity swaps than other state of the art algorithms.

Rodents play an important role in sleep studies since they are the most easily available low-cost animal species sharing most genes and gene functions with humans. The scoring of sleep stages in these studies is usually based on a manual analysis of long-lasting recordings of the brain electrical activity and the activity of skeletal muscles. Hence, there is a great need for tools automating the investigation over large experiments. In this paper we present a way of analyzing this huge amount of electrophysiological data using unsupervised learning models. We show how employing latent variable models, like Restricted Boltzmann Machines, we can define different sleep-wakefulness sub-stages reflected by regularities in the data, without any prior knowledge. Our analysis shows that we can effectively discover meaningful feature representations which characterize sleep stages at a finer level than those commonly used by the experts. These feature representations can be also used to further characterize different mouse genotypes, without incurring biases of experts like in a classical analysis setup where predefined rules limit the discovery of novel insights.

Automatic analysis of rodent behavior has been receiving growing attention in recent years since rodents have been the reference species for many neuroscientific studies, with the social interaction being among the subjects of the most important ones. Systems that are employed in these studies are mainly based on tracking of mice and activity classification through supervised learning methods, trained on datasets manually annotated by experts. In this paper, we introduce a completely unsupervised way of analysing tracking data for the automatic identification of social and non-social behaviors using models capable of spotting regularities in the data. In particular, a mean-covariance Restricted Boltzmann Machine is employed to abstract higher-level behavioral configurations of mice interacting in an arena for a long time.

Functional neuroimaging consists in the use of imaging technologies allowing to record the functional brain activity in real-time. Among all techniques, data produced by functional magnetic resonance is encoded as sequences of 3D images of thousands of voxels. The main investigation performed on this data, termed brain mapping, aims at producing functional maps of the brain. Brain mapping aims at the detection of the portion of voxels concerned with specific perceptual or cognitive brain activities. This challenge can be shaped as a problem of feature selection. Excessive features-to-instances ratio characterizing this data is a major issue for the computation of statistically robust maps. We propose a solution based on a Random Subspace Method that extends the reference approach (Search Light) adopted by the neuroscientific community. A comparison of the two methods is supported by the results of an empirical evaluation.

We propose a relational neural network defined as a special instance of the recurrent cascade correlation. The proposed model is designed to deal with classification tasks where classes are organized into generic graphs (e.g. taxonomies, ontologies etc.). The open challenge is to exploit the knowledge encoded in the relationships among the classes. This is particularly useful when there are many classes poorly represented by labeled examples. Exploiting the relationships we increase the bias, making the generalization more robust. The novelty of the proposed model can be seen from two different perspectives. On one hand, the temporal encoding of the standard recurrent networks is revised with a notion of non-stationary structural unfolding. On the other hand, it can be seen as a novel constructive algorithm that generates the neural network architecture exploiting the class structure. We present the results of an empirical evaluation on a hierarchical document classification task.

Proper reuse of learning objects depends both on the amount and quality of attached semantic metadata such as "learning objective"', "related concept", etc. Manually expressing such metadata is a time consuming and expensive task.. Here we present an approach based on a probabilistic model, which through the automatic classification of learning resources on a given taxonomic organization of the knowledge, allows to associate ontological metadata to the learning resources.

The proliferation of text documents on the web as well as within institutions necessitates their convenient organization to enable efficient retrieval of information. Although text corpora are frequently organized into concept hierarchies or taxonomies, the classification of the documents into the hierarchy is expensive in terms human effort. We present a novel and simple hierarchical Dirichlet generative model for text corpora and derive an efficient algorithm for the estimation of model parameters and the unsupervised classification of text documents into a given hierarchy. The class conditional feature means are assumed to be inter-related due to the hierarchical Bayesian structure of the model. We show that the algorithm provides robust estimates of the classification parameters by performing smoothing or regularization. We present experimental evidence on real web data that our algorithm achieves significant gains in accuracy over simpler models.

This paper propose a first approach to deal with contextual information in structured domains by recursive neural networks. The proposed model, i.e., contextual recursive cascade correlation (CRCC), a generalization of the recursive cascade correlation (RCC) model, is able to partially remove the causality assumption by exploiting contextual information stored in frozen units. We formally characterize the properties of CRCC showing that it is able to compute contextual transductions and also some causal supersource transductions that RCC cannot compute. Experimental results on controlled sequences and on a real-world task involving chemical structures confirm the computational limitations of RCC, while assessing the efficiency and efficacy of CRCC in dealing both with pure causal and contextual prediction tasks. Moreover, results obtained for the real-world task show the superiority of the proposed approach versus RCC when exploring a task for which it is not known whether the structural causality assumption holds.

Managing the hierarchical organization of data is starting to play a key role in the knowledge management community due to the great amount of human resources needed to create and maintain these organized repositories of information. Machine learning community has in part addressed this problem by developing hierarchical supervised classifiers that help maintainers to categorize new resources within given hierarchies. Although such learning models succeed in exploiting relational knowledge, they are highly demanding in terms of labeled examples, because the number of categories is related to the dimension of the corresponding hierarchy. Hence, the creation of new directories or the modification of existing ones require strong investments.This paper proposes a semi-automatic process (interleaved with human suggestions) whose aim is to minimize (simplify) the work required to the administrators when creating, modifying, and maintaining directories. Within this process, bootstrapping a taxonomy with examples represents a critical factor for the effective exploitation of any supervised learning model. For this reason we propose a method for the bootstrapping process that makes a first hypothesis of categorization for a set of unlabeled documents, with respect to a given empty hierarchy of concepts. Based on a revision of Self-Organizing Maps, namely TaxSOM, the proposed model performs an unsupervised classification, exploiting the a-priori knowledge encoded in a taxonomy structure both at the terminological and topological level. The ultimate goal of TaxSOM is to create the premise for successfully training a supervised classifier.

Recurrent neural networks fail to deal with prediction tasks which do not satisfy the causality assumption. We propose to exploit bi-causality to extend the recurrent cascade correlation model in order to deal with contextual prediction tasks. Preliminary results on artificial data show the ability of the model to preserve the prediction capability of recurrent cascade correlation on strict causal tasks, while extending this capability also to prediction tasks involving the future.

To overcome the problem of invariant pattern recognition, Simard, LeCun, and Denker (1993) proposed a successful nearest-neighbor approach based on tangent distance, attaining state-of-the-art accuracy. Since this approach needs great computational and memory effort, Hastie, Simard, and Säckinger (1995) proposed an algorithm (HSS) based on singular value decomposition (SVD), for the generation of nondiscriminant tangent models. In this article we propose a different approach, based on a gradient-descent constructive algorithm, called TD-Neuron, that develops discriminant models. We present as well comparative results of our constructive algorithm versus HSS and learning vector quantization (LVQ) algorithms. Specifically, we tested the HSS algorithm using both the original version based on the two-sided tangent distance and a new version based on the one-sided tangent distance. Empirical results over the NIST-3 database show that the TD-Neuron is superior to both SVD- and LVQ-based algorithms, since it reaches a better trade-off between error and rejection.