Accurate brain tumor segmentation is crucial for precise medical diagnosis and treatment planning in medical imaging. This research delves into assessing the effectiveness and transparency of Graph Neural Networks (GNNs) in brain tumor segmentation. The primary objectives include comparing various GNN architectures and improving their understandability by applying the GNNExplainer method. Leveraging the BraTS 2021 challenge dataset, which consists of MRI scans and corresponding ground truth annotations, the study reveals the successful application of GNNs in achieving precise brain tumor segmentation. By incorporating GNNExplainer, the explainability of the models is significantly enhanced, shedding light on the decision-making processes within the network. The proposed approach could advance the field of brain tumor segmentation, providing clinicians with accurate and transparent models to inform their decision-making processes in patient care.1

Density estimation could be viewed as a core component in machine learning, since a good estimator could be used to solve many tasks such as classification, regression, and imputing missing values. The main challenge of density estimation is balancing the model expressiveness and its learning and inference complexity. Probabilistic circuits (PCs) model a probability distribution as a computational graph. By imposing specific structural properties on such models many inference tasks become tractable. However, learning PCs usually relies on greedy and time consuming procedures. In this paper we propose a new unified approach to efficiently learn PCs having several structural properties. We introduce extremely randomized PCs (XPCs), PCs with a random structure. We show their advantage on standard density estimation benchmarks when compared to other density estimators.

BACKGROUND: Screening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features. RESULTS: For each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance. CONCLUSIONS: The best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters.

In Artificial Intelligence with Coalition Structure Generation (CSG) one refers to those cooperative complex problems that require to find an optimal partition (maximizing a social welfare) of a set of entities involved in a system. The solution of the CSG problem finds applications in many fields such as Machine Learning (set covering machines, clustering), Data Mining (decision tree, discretization), Graph Theory, Natural Language Processing (aggregation), Semantic Web (service composition), and Bioinformatics. The problem of finding the optimal coalition structure is NP-complete. In this paper we present a greedy adaptive search procedure (GRASP) with path-relinking to efficiently search the space of coalition structures. Experiments and comparisons to other algorithms prove the validity of the proposed method in solving this hard combinatorial problem.

Information Retrieval in large digital document repositories is at the same time a hard and crucial task. While the primary type of information available in documents is usually text, images play a very important role because they pictorially describe concepts that are dealt with in the document. Unfortunately, the semantic gap separating such a visual content from the underlying meaning is very wide. Additionally image processing techniques are usually very demanding in computational resources. Hence, only recently the area of Content-Based Image Retrieval has gained more attention. In this paper we describe a new technique to identify known objects in a picture based on a comparison of the shapes to known models. The comparison works by progressive approximations to save computational resources, and relies on novel algorithmic and representational solutions to improve preliminary shape extraction.

Document layout analysis is crucial in the automatic document processing workflow, because its outcome affects all subsequent processing steps. A first problem concerns the possibility of dealing not only with documents having easy layout, but with so-called non-Manhattan layout documents as well. Another problem is that most available techniques can be applied to scanned document, due to the emphasis in previous decades being put on legacy documents digitization. Conversely, nowadays most documents come directly in digital format, and thus new techniques must be developed. A famous approach proposed in the literature for layout analysis was the RLSA, suitable to scanned black&white images and based the application of Run Length Smoothing and the AND logical operator. A recent variant thereof is based on the application of the OR operator, for which reason has been called RLSO. It exploits a bottom-up approach that proved able to handle even non-Manhattan layouts, on both scanned and natively digital documents. Like RLSA, it is based on the definition of thresholds for the smoothing operator, but the different approach requires different criteria than those that work in RLSA to define proper values. Since this is a hard and unnatural task for an (even expert) user, this paper proposes a technique to automatically define such thresholds for each single document, based on the distribution of spacing therein. Application on selected samples of documents, that aimed at covering a significant landscape of real cases, revealed that the approach is satisfactory for documents characterized by the use of a uniform text font size. It can provide a useful basis also for handling more complex cases.

Layout analysis is a fundamental step in automatic document processing. Many different techniques have been proposed to perform this task. Some follow a top-down approach: they start by identifying the high level components of the page structure and then recursively split them until basic blocks are found. On the other hand, bottom-up approaches start with the smallest elements (e.g., the pixels in case of digitized document) and then recursively merge them into higher level components. A first limitation of such methods is that most of them are designed to deal only with digitized documents and hence are not applicable to native digital documents which are nowadays pervasive. Furthermore, top-down and most of bottom-up methods are able to process Manhattan layout documents only. In this work, we propose a general bottom-up strategy to tackle the layout analysis of (possibly) non-Manhattan documents, and two specializations of it to handle both bitmap and PS/PDF sources. It was successfully embedded and tested in the DOMINUS document management system.

First-Order Logic formulæ are a powerful representation formalism characterized by the use of relations, that cause serious computational problems due to the phenomenon of indeterminacy (various portions of one description are possibly mapped in different ways onto another description). Being able to identify the correct corresponding parts of two descriptions would help to tackle the problem: hence, the need for a framework for the comparison and similarity assessment. This could have many applications in Artificial Intelligence: guiding subsumption procedures and theory revision systems, implementing flexible matching, supporting instance-based learning and conceptual clustering. Unfortunately, few works on this subject are available in the literature. This paper focuses on Horn clauses, which are the basis for the Logic Programming paradigm, and proposes a novel similarity formula and evaluation criteria for identifying the descriptions components that are more similar and hence more likely to correspond to each other, based only on their syntactic structure. Experiments on real-world datasets prove the effectiveness of the proposal, and the efficiency of the corresponding implementation in the above tasks.

In real-world digital libraries, artificial intelligence techniques are essential for tackling the automatic document processing task with sufficient flexibility. The great variability in document kind, content and shape requires powerful representation formalisms to catch all the domain complexity. The continuous flow of new documents requires adaptable techniques that can progressively adjust the acquired knowledge on documents as long as new evidence becomes available, even extending if needed the set of recognized document types. Both these issues have not yet been thoroughly studied. This paper presents an incremental first-order logic learning framework for automatically dealing with various kinds of evolution in digital repositories content: evolution in the definition of class definitions, evolution in the set of known classes and evolution by addition of new unknown classes. Experiments show that the approach can be applied to real-world.

Organizing large repositories spread throughout the most diverse Web sites rises the problem of effective storage and efficient retrieval of documents. This can be obtained by selectively extracting from them the significant textual information, contained in peculiar layout components, that in turn depend on the identification of the correct document class. The continuous flow of new and different documents in a weakly structured environment like the Web calls for in- crementality, as the ability to continuously update or revise a faulty knowledge previously acquired, while the need to express structural relations among layout components suggest the exploitation of a powerful and symbolic representation language. This paper proposes the application of incremental first-order logic learning techniques in the document layout preprocessing steps, supported by good results obtained in experiments on a real dataset.

Digital repositories raise the need for an effective and efficient retrieval of the stored material. In this paper, we propose the intensive application of intelligent techniques to the steps of document layout analysis, document image classification and understanding on digital documents. Specifically, the complex interrelation existing among layout components, that are fundamental to assign them the proper semantic role, suggest the exploitation of first-order representations in some learning steps. Results obtained in a prototypical system for scientific conference management prove that the proposed approach can be beneficial both for the layout recognition and for the selection of interesting components of the document, from which extracting the text for categorizing the document according to its topic.