On the use of multi–objective evolutionary classifiers for breast cancer detection

Purpose Breast cancer is one of the most common tumours in women, nevertheless, it is also one of the cancers that is most usually treated. As a result, early detection is critical, which can be accomplished by routine mammograms. This paper aims to describe, analyze, compare and evaluate three image descriptors involved in classifying breast cancer images from four databases. Approach Multi–Objective Evolutionary Algorithms (MOEAs) prove themselves as being efficient methods for selection and classification problems. This paper aims to study combinations of well–known classification objectives in order to compare the results of their application in solving very specific learning problems. The experimental results undergo empirical analysis which is supported by a statistical approach. The results are illustrated on a collection of medical image databases, but with a focus on the MOEAs’ performance in terms of several well–known measures. The databases were chosen specifically to feature reliable human annotations, so as to measure the correlation between the gold standard classifications and the various MOEA classifications. Results We have seen how different statistical tests rank one algorithm over the others in our set as being better. These findings are unsurprising, revealing that there is no single gold standard for comparing diverse techniques or evolutionary algorithms. Furthermore, building meta-classifiers and evaluating them using a single, favorable metric is both extremely unwise and unsatisfactory, as the impact is to skew the results. Conclusions The best method to address these flaws is to select the right set of objectives and criteria. Using accuracy-related objectives, for example, is directly linked to maximizing the number of true positives. If, on the other hand, accuracy is chosen as the generic metric, the primary classification goal is shifted to increasing the positively categorized data points

Feasibility of Automatic Seed Generation Applied to Cardiac MRI Image Analysis

We present a method of using interactive image segmentation algorithms to reduce specific image segmentation problems to the task of finding small sets of pixels identifying the regions of interest. To this end, we empirically show the feasibility of automatically generating seeds for GrowCut, a popular interactive image segmentation algorithm. The principal contribution of our paper is the proposal of a method for automating the seed generation method for the task of whole-heart segmentation of MRI scans, which achieves competitive unsupervised results (0.76 Dice on the MMWHS dataset). Moreover, we show that segmentation performance is robust to seeds with imperfect precision, suggesting that GrowCut-like algorithms can be applied to medical imaging tasks with little modeling effort

STUDY OF CONNECTION BETWEEN ARTICULATION POINTS AND NETWORK MOTIFS IN COMPLEX NETWORKS

Complex networks are powerful mechanisms one can use to model real-world networks as topological spaces. The beauty of these structures is provided by the infinite degree of analysis one is allowed to do using them. Biologically it is almost impossible for the human mind to comprehend the behaviour of these systems, but when modelled as complex networks different properties of the network topology can reveal precious information. Starting from the two key properties of the participants n a complex network and the relations between them, one can derive further properties that reflect specific behaviours for entities or groups of entities. Examples of these further remarkable properties include entities which create unique bridges between two or more communities (known as Articulation Points) or the appearance of patterns of interconnections between entities (known as Network Motifs). Our paper performs a study on the co-existence of these two properties, Articulation Points and Network Motifs, and how their appearance is correlated, by using results obtained in analysing a variety of real-world networks

A Transfer Learning Approach on the Optimization of Edge Detectors for Medical Images Using Particle Swarm Optimization

Edge detection is a fundamental image analysis task, as it provides insight on the content of an image. There are weaknesses in some of the edge detectors developed until now, such as disconnected edges, the impossibility to detect branching edges, or the need for a ground truth that is not always accessible. Therefore, a specialized detector that is optimized for the image particularities can help improve edge detection performance. In this paper, we apply transfer learning to optimize cellular automata (CA) rules for edge detection using particle swarm optimization (PSO). Cellular automata provide fast computation, while rule optimization provides adaptability to the properties of the target images. We use transfer learning from synthetic to medical images because expert-annotated medical data is typically difficult to obtain. We show that our method is tunable for medical images with different properties, and we show that, for more difficult edge detection tasks, batch optimization can be used to boost the quality of the edges. Our method is suitable for the identification of structures, such as cardiac cavities on medical images, and could be used as a component of an automatic radiology decision support tool

The Innovative Use of Intelligent Chatbot for Sustainable Health Education Admission Process: Learnt Lessons and Good Practices

This article presents the methodology of creation of an innovative used by intelligent chatbots which support the admission process in universities. The lifecycle of the ontology, unlike the classical lifecycles, has six stages: conceptualization, formalization, development, testing, production and maintenance. This leads to sustainability of the chatbot, called Ana, which has been implemented at the “Iuliu Hatieganu” University of Medicine and Pharmacy from Cluj-Napoca during the admission session throughout July–September 2022, for international candidates. The sustainability of the chatbot comes from the continuous maintenance and updates of the ontology, based on candidates’ interraction with the system and updates of the admission procedures. Over time, the chatbot is able to answer the questions according to the present situation of the admission and the real needs of the candidates. Ana had a huge impact, succeeding to resolve a number of 5173 applicants requests, and only 809 messages was transferred to the human operators, statistics which show a high cost-benefit improvement in terms of reducing the travel expenses for the candidates and also for the university. The article also summarizes the good practices in developing and use of such an intelligent chatbot

Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning

Despite the promising results obtained by deep learning methods in the field of medical image segmentation, lack of sufficient data always hinders performance to a certain degree. In this work, we explore the feasibility of applying deep learning methods on a pilot dataset. We present a simple and practical approach to perform segmentation in a 2D, slice-by-slice manner, based on region of interest (ROI) localization, applying an optimized training regime to improve segmentation performance from regions of interest. We start from two popular segmentation networks, the preferred model for medical segmentation, U-Net, and a general-purpose model, DeepLabV3+. Furthermore, we show that ensembling of these two fundamentally different architectures brings constant benefits by testing our approach on two different datasets, the publicly available ACDC challenge, and the imATFIB dataset from our in-house conducted clinical study. Results on the imATFIB dataset show that the proposed approach performs well with the provided training volumes, achieving an average Dice Similarity Coefficient of the whole heart of 89.89% on the validation set. Moreover, our algorithm achieved a mean Dice value of 91.87% on the ACDC validation, being comparable to the second best-performing approach on the challenge. Our approach provides an opportunity to serve as a building block of a computer-aided diagnostic system in a clinical setting

The experience of giving birth in a hospital in Spain: humanization versus technification

Aim: to explore and describe the experiences of women giving birth in a tertiary public hospital, with special focus on experiences related to humanized care and women's participation in decision making. Method: this is a qualitative phenomenological study through semi-structured interviews to postpartum women giving birth in a tertiary hospital between January and May 2017. Data were analysed through content analysis. Results: the two overarching themes emerged were the professional-information dyad and privacy. Subthemes of the first main theme were the therapeutic relationship, decision-making, feeding the baby, procedures, and the time factor. Subthemes of the second topic were the feelings generated by the hospital environment, the delivery room, and the maternity ward. Conclusions: if the therapeutic relationship is good, technology is not seen as dehumanising but rather as necessary to ensure continuing safety. "Humanising" material resources are not a priority for women in the birth process and are little used. Privacy was experienced as being a particularly intense need, which women called for throughout the healthcare process