Abstract
Backed by more powerful computational resources and optimized training routines, Deep Learning models have proven unprecedented performance and several benefits to extract information from chest X-ray data. This is one of the most common imaging exams, whose increasing demand is reflected in the aggravated radiologists’ workload. Consequently, healthcare would benefit from computer-aided diagnosis systems to prioritize certain exams and further identify possible pathologies. Pioneering work in chest X-ray analysis has focused on the identification of specific diseases, but to the best of the authors’ knowledge no paper has specifically reviewed relevant work on abnormality detection and multi-label thoracic pathology classification. This paper focuses on those issues, selecting the leading chest X-ray based deep learning strategies for comparison. In addition, the paper discloses the current annotated public chest X-ray databases, covering the common thorax diseases.

Abstract
Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart Foundation (NHF) of New Zealand Health Research Council (HRC) of New Zealand Artificial intelligence shows considerable promise for automated analysis and interpretation of medical images, particularly in the domain of cardiovascular imaging. While application to cardiac magnetic resonance (CMR) has demonstrated excellent results, automated analysis of 3D echocardiography (3D-echo) remains challenging, due to the lower signal-to-noise ratio (SNR), signal dropout, and greater interobserver variability in manual annotations. As 3D-echo is becoming increasingly widespread, robust analysis methods will substantially benefit patient evaluation.  We sought to leverage the high SNR of CMR to provide training data for a convolutional neural network (CNN) capable of analysing 3D-echo. We imaged 73 participants (53 healthy volunteers, 20 patients with non-ischaemic cardiac disease) under both CMR and 3D-echo (<1 hour between scans). 3D models of the left ventricle (LV) were independently constructed from CMR and 3D-echo, and used to spatially align the image volumes using least squares fitting to a cardiac template. The resultant transformation was used to map the CMR mesh to the 3D-echo image. Alignment of mesh and image was verified through volume slicing and visual inspection (Fig. 1) for 120 paired datasets (including 47 rescans) each at end-diastole and end-systole. 100 datasets (80 for training, 20 for validation) were used to train a shallow CNN for mesh extraction from 3D-echo, optimised with a composite loss function consisting of normalised Euclidian distance (for 290 mesh points) and volume. Data augmentation was applied in the form of rotations and tilts (<15 degrees) about the long axis. The network was tested on the remaining 20 datasets (different participants) of varying image quality (Tab. I). For comparison, corresponding LV measurements from conventional manual analysis of 3D-echo and associated interobserver variability (for two observers) were also estimated. Initial results indicate that the use of embedded CMR meshes as training data for 3D-echo analysis is a promising alternative to manual analysis, with improved accuracy and precision compared with conventional methods. Further optimisations and a larger dataset are expected to improve network performance. (n?=?20) LV EDV (ml) LV ESV (ml) LV EF (%) LV mass (g) Ground truth CMR 150.5 ± 29.5 57.9 ± 12.7 61.5 ± 3.4 128.1 ± 29.8 Algorithm error -13.3 ± 15.7 -1.4 ± 7.6 -2.8 ± 5.5 0.1 ± 20.9 Manual error -30.1 ± 21.0 -15.1 ± 12.4 3.0 ± 5.0 Not available Interobserver error 19.1 ± 14.3 14.4 ± 7.6 -6.4 ± 4.8 Not available Tab. 1. LV mass and volume differences (means ± standard deviations) for 20 test cases. Algorithm: CNN – CMR (as ground truth). Abstract Figure. Fig 1. CMR mesh registered to 3D-echo.

Abstract
Cancer is a leading cause of death worldwide. The detection and diagnosis of most cancers are confirmed by a tissue biopsy that is analyzed via the optic microscope. These samples are then scanned to giga-pixel sized images for further digital processing by pathologists. An automated method to segment the malignant regions of these images could be of great interest to detect cancer earlier and increase the agreement between specialists. However, annotating these giga-pixel images is very expensive, time-consuming and error-prone. We evaluate 4 existing annotation efficient methods, including transfer learning and self-supervised learning approaches. The best performing approach was to pretrain a model to colourize a grayscale histopathological image and then finetune that model on a dataset with manually annotated examples. This method was able to improve the Intersection over Union from 0.2702 to 0.3702.

Abstract
Introduction: Heart failure (HF) represents a huge financial and economic burden worldwide. Some authors advocate that remote monitoring should be implemented to improve HF management, but given its increasing incidence, as well as its morbidity and mortality, a question still remains: are we monitoring it properly? There is no shortage of literature on home monitoring devices, however, most of them are designed to monitor an unsuitable array of variables and, to the best of our knowledge, there are no large randomized studies about their impact on morbidity/mortality of HF patients. Objective: Description of a novel monitoring device. Methods: As a solution, we designed MONITORIA (MOnitoring NonInvasively To Overcome mortality Rates of heart Insufficiency on Ambulatory). Results: This is a multimodal device that will provide real time monitoring of vital, electrophysiological, hemodynamic and chemical signs, transthoracic impedance, and physical activity levels. The device is meant to perform continuous analysis and transmission of all data. Significant alterations in a patient's variable will alert the attending physician and, in case of potentially life-threatening situations, the national emergency medical system. The MONITORIA device will, also, have a function that sends shocks or functions as a pacemaker to treat certain arrhythmias/blockades. This function can be activated the very first time the patient utilizes it, based on their risk of sudden cardiac death. Discussion/Conclusions: MONITORIA is a promising device mostly because it is included in a follow-up program that takes into account a multi-perspective feature of HF development and is based on the real world patient, adapting innovations not to the disease but rather to the patients. (C) 2021 Sociedade Portuguesa de Cardiologia. Published by Elsevier Espana, S.L.U.

Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Background   Diffuse interstitial or myocardial replacement fibrosis are common features of a large variety of cardiomyopathies. These alterations contribute to functional changes, particularly to an increased myocardial stiffness (MS). Histological examination is the gold standard for myocardial fibrosis quantification, however, it requires endomyocardial biopsy which is invasive and not without risks. Cardiac magnetic resonance (CMR) can characterize the extent of both diffuse and replacement fibrosis and may have prognostic value in various cardiomyopathies. Echocardiographic shear wave (SW) elastography is an emerging approach for measuring MS in vivo. SWs occur after mechanical excitation of the myocardium, e.g. after mitral valve closure (MVC), and their propagation velocity is directly related to MS, thus providing an opportunity to assess stiffness at end-diastole. Purpose The aim was to investigate if velocities of natural SW can distinguish between interstitial and replacement fibrosis.  Methods We prospectively enrolled 47 patients (22 patients after heart transplant [54.2?±?15.8 years, 82.6% male] and 25 patients with established hypertrophic cardiomyopathy [54.0?±?13.5 years, 80.0% male]) undergoing CMR during their check-up. We performed SW elastography in parasternal long axis views of the LV using a fully programmable experimental scanner (HD-PULSE) equipped with a clinical phased array transducer (Samsung Medison P2-5AC) at 1100?±?250 frames per second. Tissue acceleration maps were extracted from an anatomical M-mode line along the midline of the LV septum. The SW propagation velocity at MVC was measured as the slope in the M-mode image. All patients underwent T1 mapping as well as late gadolinium enhancement (LGE) cardiac magnetic resonance at 1.5 T to assess the presence of diffuse or replacement fibrosis (Figure A). Therefore, patients were divided in three groups: no fibrosis, diffuse fibrosis and replacement fibrosis. Results Mechanical SW’s were observed in 46 subjects starting immediately after MVC and propagating from the LV base to the apex. SW propagation velocity at MVC correlated well with native myocardial T1 values (r?=?0.65, p?<?0.0001) and differed significantly among groups (p?<?0.0001), with a significant post-test between any pair of groups (Figure B). SW velocities below a cut-off of 6.01 m/s showed the highest accuracy to identify patients without any type of fibrosis (sensitivity 88 %, specificity 89%, area under the curve?=?0.93) (Figure C). A cut-off of 8.11 m/s could distinguish replacement fibrosis from diffuse fibrosis with a sensitivity and specificity of 59% and 92 %, respectively (area under the curve?=?0.80) (Figure D). Conclusions   Shear wave velocities after mitral valve closure can distinguish between normal and pathological myocardium and can detect differences between diffuse and replacement fibrosis. Abstract Figure.

Abstract
Heart Period (H) results from the activity of several coexisting control mechanisms, involving Systolic Arterial Pressure (S) and Respiration (R), which operate across multiple time scales encompassing not only short-term dynamics but also long-range correlations. In this work, multiscale representation of Transfer Entropy (TE) and of its decomposition in the network of these three interacting processes is obtained by extending the multivariate approach based on linear parametric VAR models to the Vector AutoRegressive Fractionally Integrated (VARFI) framework for Gaussian processes. This approach allows to dissect the different contributions to cardiac dynamics accounting for the simultaneous presence of short and long term dynamics. The proposed method is first tested on simulations of a benchmark VARFI model and then applied to experimental data consisting of H, S and R time series measured in healthy subjects monitored at rest and during mental and postural stress. The results reveal that the proposed method can highlight the dependence of the information transfer on the balance between short-term and long-range correlations in coupled dynamical systems.