Large Vessel Occlusion is one of the most critical neurological emergencies in stroke care, requiring rapid and accurate diagnosis to optimize clinical outcomes. Automated detection tools have demonstrated the potential to significantly reduce treatment time, thereby improving patient prognosis. In this paper, we propose a novel two-stage deep learning approach for detecting large vessel occlusion and assessing its volume directly from computed tomography angiography. The first stage employs a two-dimensional convolutional neural network-based detector, built upon GravityNet, specifically adapted for single lesion detection with a novel pixel-based configuration. The second stage applies a three-dimensional false-positive reduction technique to refine predictions within the brain volume. Our method achieves 80% sensitivity at two false positives per case, demonstrating its robustness and effectiveness in detecting large vessel occlusions on computed tomography angiography.

The use of Machine Learning in IoT devices has become the only viable path in today's landscape, where millions of connected devices surround us and increasingly affect our lives. These resource-limited devices interact with the surrounding world via actuators and sensors. Many of these devices use Machine Learning techniques to be able to interpret the world and choose the appropriate action to take. Therefore the purpose of this work is to create a system that allows the application of Machine Learning algorithms directly to the ends of the network, where sensors and actuators reside. The system is designed to rely on the SENSIPLUS smart-sensor as a data acquisition device, and consists of an automatic code generation and compilation system, which through the use of a Toolchain, allows to run artificial intelligence algorithms directly on microcontroller devices.

Water monitoring systems continuously working ensure real–time pollutant detection capabilities according to their sensitivity and specificity. It is necessary to balance such features because, although being able to sense several substances is a desired feature, the reduction of false positives is a primary goal a classification system should have. High false positive makes the system unusable. The current solution enables a 24/7 service with a sampling rate equal to 0.6 Hz. Our goal is to limit false positives to 1 per day, thus achieving 99.99% accuracy at least. In this paper, we add a false positive reduction module to our pre-existent system, aiming to manage false positive boosters as sensor drift and signal oscillations. Obtained results, using a Multi Layer Perceptron classifier, confirm the false positive reduction while keeping high true positive rates.

Early detection of Alzheimer's Disease (AD) is essential in order to initiate therapies that can reduce the effects of such a disease, improving both life quality and life expectancy of patients. Among all the activities carried out in our daily life, handwriting seems one of the first to be influenced by the arise of neurodegenerative diseases. For this reason, the analysis of handwriting and the study of its alterations has become of great interest in this research field in order to make a diagnosis as early as possible. In recent years, many studies have tried to use classification algorithms applied to handwriting to implement decision support systems for AD diagnosis. A key issue for the use of these techniques is the detection of effective features, that allow the system to distinguish the natural handwriting alterations due to age, from those caused by neurodegenerative disorders. In this context, many interesting results have been published in the literature in which the features have been typically selected by hand, generally considering the dynamics of the handwriting process in order to detect motor disorders closely related to AD. Features directly derived from handwriting generation models can be also very helpful for AD diagnosis. It should be remarked, however, that the above features do not consider changes in the shape of handwritten traces, which may occur as a consequence of neurodegenerative diseases, as well as the correlation among shape alterations and changes in the dynamics of the handwriting process. Moving from these considerations, the aim of this study is to verify if the combined use of both shape and dynamic features allows a decision support system to improve performance for AD diagnosis. To this purpose, starting from a database of on-line handwriting samples, we generated for each of them a synthetic off-line colour image, where the colour of each elementary trait encodes, in the three RGB channels, the dynamic information associated to that trait. Finally, we exploited the capability of Deep Neural Networks (DNN) to automatically extract features from raw images, following the Transfer Learning approach. The experimental comparison of the results obtained by using standard features and features extracted according the above procedure, confirmed the effectiveness of our approach.

In smart city framework, the water monitoring through an efficient, low-cost, low-power and IoT-oriented sensor technology is a crucial aspect to allow, with limited resources, the analysis of contaminants eventually affecting wastewater. In this sense, common interfering substances, as detergents, cannot be classified as dangerous contaminants and should be neglected in the classification. By adopting classical machine learning approaches having a finite set of possible responses, each alteration of the sensor baseline is always classified as one out of the predetermined substances. Consequently, we developed an anomaly detection system based on one-class classifiers, able to discriminate between a recognized set of substances and an interfering source. In this way, the proposed detection system is able to provide detailed information about the water status and distinguish between harmless detergents and dangerous contaminants.

Nowadays water monitoring represents one of the most challenging global aims for the protection of people and environment health. In this paper we propose the application of an integrated system for the detection and recognition of contaminants in water. It is based on a two layer architecture: a sensing layer based on SENSIPLUS chip, and a data collection and classification layer, hereafter referred as SENSIPLUS Deep Machine (SDM). The SDM includes: a Micro Controller Unit (MCU), an optional host controller (e.g. laptop, smartphone, etc.) and different software components for data communication, analysis, and classification/regression based on machine learning techniques. Although the SDM classification/regression module can be potentially developed with any machine learning solution, in this paper we adopted an Artificial Neural Network with only one hidden layer to have a lightweight solution suitable to run (for inference) on ultra low power MCU. Aiming at further minimizing the network complexity, two alternative training sessions have been pursued: the first one using raw sensors' data and the second one applying a feature space dimensionality reduction through the Principal Component Analysis technique. Comparable and positive results (higher than 82% as average accuracy) have been obtained, confirming the validity and potentiality of the proposed system.

In this paper, we analyze how stabilizing the variance of intensity-dependent quantum noise in digital mammograms can significantly improve the computerized detection of microcalcifications (MCs). These lesions appear on mammograms as tiny deposits of calcium smaller than 20 pixels in diameter. At this scale, high frequency image noise is dominated by quantum noise, which in raw mammograms can be described with a square-root noise model. Under this assumption, we derive an adaptive variance stabilizing transform (VST) that stabilizes the noise to unitary standard deviation in all the images. This is achieved by estimating the noise characteristics from the image at hand. We tested the adaptive VST as a preprocessing stage for four existing computerized MC detection methods on three data sets acquired with mammographic units from different manufacturers. In all the test cases considered, MC detection performance on transformed mammograms was statistically significantly higher than on unprocessed mammograms. Results were also superior in comparison with a "fixed" (nonparametric) VST previously proposed for digital mammograms.

Microcalcifications are an early mammographic indicator of breast cancer. To assist screening radiologists in reading mammograms, machine learning techniques have been developed for the automated detection of microcalcifications. In the last few years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision and medical image analysis applications. A key step in CNN-based detection is image preprocessing, including brightness and contrast variations. In this work, we investigate the influence of preprocessing of digital mammograms on the microcalcification detection performance of two CNNs inspired by the popular AlexNet and VGGnet. We tested two preprocessing methods commonly applied to unprocessed raw digital mammograms: (i) the logarithmic transformation adopted by different manufacturers for the presentation of the image to the radiologists; and (ii) the square-root of image intensity that stabilizes the intensity-dependent noise present in the mammogram. Experiments were performed on 1,066 mammograms acquired with GE Senographe systems. Both preprocessing methods yielded statistically significantly better microcalcification detection performance. Results of the square-root transform were superior to those obtained with the log transform.

Assessment of retinal vessels is fundamental for the diagnosis of many disorders such as heart diseases, diabetes and hypertension. The imaging of retina using advanced fundus camera has become a standard in computer-assisted diagnosis of opthalmic disorders. Modern cameras produce high quality color digital images, but during the acquisition process the light reflected by the retinal surface generates a luminosity and contrast variation. Irregular illumination can introduce severe distortions in the resulting images, decreasing the visibility of anatomical structures and consequently demoting the performance of the automated segmentation of these structures. In this paper, a novel approach for illumination correction of color fundus images is proposed and applied as preprocessing step for retinal vessel segmentation. Our method builds on the connection between two different phenomena, shadows and haze, and works by removing the haze from the image in the inverted intensity domain. This is shown to be equivalent to correct the nonuniform illumination in the original intensity domain. We tested the proposed method as preprocessing stage of two vessel segmentation methods, one unsupervised based on mathematical morphology, and one supervised based on deep learning Convolutional Neural Networks (CNN). Experiments were performed on the publicly available retinal image database DRIVE. Statistically significantly better vessel segmentation performance was achieved in both test cases when illumination correction was applied.

Error Correcting Output Coding (ECOC) is a widely used and successful technique that implements a classification system by splitting a multiclass problem into a set of dichotomies according to a coding matrix. In this paper we propose a new approach for the ECOC systems based on a well-known family of error correcting codes in the Coding Theory: the Low Density Parity Check (LDPC) codes. The goal is twofold: first, to introduce a new coding strategy for determining the code words to be collected in a coding matrix. Second, to exploit the algebraic properties of LDPC codes for recovering the bit errors in the output word and increasing the performance of the classification system. We compare the proposed technique with other commonly used coding strategies on some benchmark data sets achieving very interesting results.

When mammograms are analyzed through a Computer Aided Diagnosis (CAD) system the presence of the pectoral muscle can affect the results of the automatic detection of breast lesions. This problem is particularly evident in mediolateral oblique (MLO) view where the pectoral muscle appears as a high intensity region across the margin of the mammogram. An automatic identification of the pectoral muscle is an essential step because of its similar characteristics with the abnormal tissue that can interfere with the detection of suspicious regions or bias the estimation of breast tissue density. This paper presents a new approach for the detection of pectoral muscle in MLO view of the mammo-graphic images. It is based on a preprocessing step useful to normalize the image and highlight the boundary between the muscle and the mammary tissue. A subsequent step including edge detection and regression via RANSAC provides the final contour of the muscle area. The experiments performed on a standard data set show very encouraging results.

The presence of clusters of microcalcifications in mammograms is particularly significant for early detection of breast cancer. In this paper a Computer Aided Detection system designed for this task is described. The detection of microcalcifications is performed by means of a segmentation based on a watershed transform and a further analysis based both on heuristic rules and AdaBoost classification. Finally a clustering algorithm is applied to detect those clusters of medical interest. The approach has been successfully tested on a Full Field Digital Mammographic database that has been developed through a strong cooperation between radiologists and computer scientists.

Mammography is a noninvasive diagnostic technique widely used for early cancer detection in women breast. The automatic detection and classification of some abnormalities in mammograms is of challenging scientific and technical interest because of the associated research topics and the potential clinical applications. Therefore, the design of a medical CAD (Computer-Aided Diagnosis or Detection) system for the analysis of mammographies involves a team composed by both medical and technical experts. In this paper we show the interaction that occurs between computer scientists and radiologists in the development and evaluation of a CAD system for the detection of clusters of micro-calcifications.

In this paper, we propose a method for the linear combination of several dichotomizers aimed at maximizing the area under the receiver operating characteristic (ROC) curve of the resulting classification system. This is particularly suited for real applications where it is difficult to exactly determine the key parameters such as costs and priors. In such cases, the accuracy is not adequate in measuring the quality of a classification system, while the ROC analysis provides the right tools for an appropriate assessment of the classification performance. The proposed approach revealed to be particularly effective with respect to other widespread combination rules both on artificial and real applications.

Error Correcting Output Coding is a common technique for multiple class classification tasks which decomposes the original problem in several two-class problems solved through dichotomizers. Such classification system can be improved with a reject option which can be defined according to the level of information available from the dichotomizers. This paper analyzes how this knowledge is useful when applying such reject rules. The nature of the outputs, the kind of the employed classifiers and the knowledge of their loss function are influential details for the improvement of the general performance of the system. Experimental results on popular benchmark data sets are reported to show the behavior of the different schemes.

Mammography is a not invasive diagnostic technique widely used for early cancer detection in women breast. A particularly significant clue of such disease is the presence of clusters of microcalcifications. The automatic detection and classification of such clusters is a very difficult task because of the small size of the microcalcifications and of the poor quality of the digital mammograms. In literature, all the proposed methods for the automatic detection focus on the single microcalcification. In this paper, an approach that moves the final decision on the regions identified by the segmentation in the phase of clustering is proposed. To this aim, the output of a classifier on the single microcalcifications is used as input data in a clustering algorithms which produce the detected clusters. As final output the system highlights the suspicious clusters, leaving to the specialist the diagnosis responsibility. The approach has been successfully tested on a standard database of 40 mammographic images, publicly available.

The class imbalance is a critical problem in classification tasks related to many real world applications. A large number of solutions were proposed in literature, both at the algorithmic and data levels. In this paper we analyze the second kind of approach and, in particular, we focus our attention on the use of Multiple Classification Systems where each classifier is trained on a dataset containing the minority class and a subset of the majority class samples. The aim of this approach is to avoid the drawbacks of other methods, commonly used in this context, which force a balanced distribution by oversampling the minority class. We compare the results obtained applying different realizations of the method on the UCI Repository datasets.