Abstract
BackgroundBreast cancer therapy improved significantly, allowing for different surgical approaches for the same disease stage, therefore offering patients different aesthetic outcomes with similar locoregional control. The purpose of the CINDERELLA trial is to evaluate an artificial-intelligence (AI) cloud-based platform (CINDERELLA platform) vs the standard approach for patient education prior to therapy. MethodsA prospective randomized international multicentre trial comparing two methods for patient education prior to therapy. After institutional ethics approval and a written informed consent, patients planned for locoregional treatment will be randomized to the intervention (CINDERELLA platform) or controls. The patients in the intervention arm will use the newly designed web-application (CINDERELLA platform, CINDERELLA APProach) to access the information related to surgery and/or radiotherapy. Using an AI system, the platform will provide the patient with a picture of her own aesthetic outcome resulting from the surgical procedure she chooses, and an objective evaluation of this aesthetic outcome (e.g., good/fair). The control group will have access to the standard approach. The primary objectives of the trial will be i) to examine the differences between the treatment arms with regards to patients' pre-treatment expectations and the final aesthetic outcomes and ii) in the experimental arm only, the agreement of the pre-treatment AI-evaluation (output) and patient's post-therapy self-evaluation. DiscussionThe project aims to develop an easy-to-use cost-effective AI-powered tool that improves shared decision-making processes. We assume that the CINDERELLA APProach will lead to higher satisfaction, better psychosocial status, and wellbeing of breast cancer patients, and reduce the need for additional surgeries to improve aesthetic outcome.

Abstract
Morphing attacks keep threatening biometric systems, especially face recognition systems. Over time they have become simpler to perform and more realistic, as such, the usage of deep learning systems to detect these attacks has grown. At the same time, there is a constant concern regarding the lack of interpretability of deep learning models. Balancing performance and interpretability has been a difficult task for scientists. However, by leveraging domain information and proving some constraints, we have been able to develop IDistill, an interpretable method with state-of-the-art performance that provides information on both the identity separation on morph samples and their contribution to the final prediction. The domain information is learnt by an autoencoder and distilled to a classifier system in order to teach it to separate identity information. When compared to other methods in the literature it outperforms them in three out of five databases and is competitive in the remaining. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

Abstract
The preservation of many music works produced in the past entails their digitalization and consequent accessibility in an easy-to-manage digital format. Carrying this task manually is very time consuming and error prone. While optical music recognition systems usually perform well on printed scores, the processing of handwritten musical scores by computers remain far from ideal. One of the fundamental stages to carry out this task is the staff line detection. In this paper a new method for the automatic detection of music staff lines based on a connected path approach is presented. Lines affected by curvature, discontinuities, and inclination are robustly detected. Experimental results show that the proposed technique consistently outperforms well-established algorithms.

Abstract
Many musical works produced in the past are still currently available only as original manuscripts or as photocopies. The preservation of these works requires their digitalization and transformation into a machine-readable format. However, and despite the many research activities on optical music recognition (OMR), the results for handwritten musical scores are far from ideal. Each of the proposed methods lays the emphasis on different properties and therefore makes it difficult to evaluate the efficiency of a proposed method. We present in this article a comparative study of several recognition algorithms of music symbols. After a review of the most common procedures used in this context, their respective performances are compared using both real and synthetic scores. The database of scores was augmented with replicas of the existing patterns, transformed according to an elastic deformation technique. Such transformations aim to introduce invariances in the prediction with respect to the known variability in the symbols, particularly relevant on handwritten works. The following study and the adopted databases can constitute a reference scheme for any researcher who wants to confront a new OMR algorithm face to well-known ones.

Abstract
Two programs were recently developed for the aesthetic evaluation of results in breast cancer conservative treatment: the Breast Cancer Conservative Treatment cosmetic results (BCCT.core) and the Breast Analyzing Tool (BAT). Both make use of a face-only photographic view of the patient and were developed to overcome the lack of reproducibility observed with subjective visual evaluation. The BCCT.core analyses several parameters related to asymmetry, color differences and scar appearance, while the BAT considers only asymmetry measurements. The purpose of this study was to compare the performance of these two methods. Material and methods Digital pictures of 59 patients from Porto and 60 from Vienna were evaluated subjectively by two panels using the four-class Harris scale. The Porto photographs had a similar backlight and better quality, and were evaluated by an international panel of 23 experts. The Vienna photographs had different backlight and lower quality, and were evaluated by four students and two breast cancer specialists. All 119 cases were submitted to analysis using the BCCT.core and BAT. Agreement between the software programs and the subjective evaluation was calculated using kappa (k), weighted kappa statistics (wk) and error rate (er). Results In overall analysis, BCCT.core program obtained a better agreement with the subjective evaluation (k = 0.56; wk = 0,64; er = 0.20) than the BAT software (k = 0.39; wk = 0.46; er = 0.42) (P < 0.0007). Results were again better for the BCCT.core program, when analysing the photographs obtained in Porto (k = 0.71; wk = 0.78; er = 0.14) than for the BAT (k = 0.35; wk = 0.41; er = 0.51) (P < 0.0003) while no significant differences in agreement were obtained regarding the Vienna images (P > 0.1). Conclusions The results suggest that the inclusion of multiple parameters in image analyses of aesthetic results has the potential to improve results. However, picture quality is probably important for analysis of other features besides asymmetry.

Abstract