Abstract
Cardiovascular imaging studies provide a multitude of structural and functional data to better understand disease mechanisms. While pooling data across studies enables more powerful and broader applications, performing quantitative comparisons across datasets with varying acquisition or analysis methods is problematic due to inherent measurement biases specific to each protocol. We show how dynamic time warping and partial least squares regression can be applied to effectively map between left ventricular geometries derived from different imaging modalities and analysis protocols to account for such differences. To demonstrate this method, paired real-time 3D echocardiography (3DE) and cardiac magnetic resonance (CMR) sequences from 138 subjects were used to construct a mapping function between the two modalities to correct for biases in left ventricular clinical cardiac indices, as well as regional shape. Leave-one-out cross-validation revealed a significant reduction in mean bias, narrower limits of agreement, and higher intraclass correlation coefficients for all functional indices between CMR and 3DE geometries after spatiotemporal mapping. Meanwhile, average root mean squared errors between surface coordinates of 3DE and CMR geometries across the cardiac cycle decreased from 7 +/- 1 to 4 +/- 1 mm for the total study population. Our generalised method for mapping between time-varying cardiac geometries obtained using different acquisition and analysis protocols enables the pooling of data between modalities and the potential for smaller studies to leverage large population databases for quantitative comparisons.

Abstract
Chest X-ray scans are frequently requested to detect the presence of abnormalities, due to their low-cost and non-invasive nature. The interpretation of these images can be automated to prioritize more urgent exams through deep learning models, but the presence of image artifacts, e.g. lettering, often generates a harmful bias in the classifiers and an increase of false positive results. Consequently, healthcare would benefit from a system that selects the thoracic region of interest prior to deciding whether an image is possibly pathologic. The current work tackles this binary classification exercise, in which an image is either normal or abnormal, using an attention-driven and spatially unsupervised Spatial Transformer Network (STERN), that takes advantage of a novel domain-specific loss to better frame the region of interest. Unlike the state of the art, in which this type of networks is usually employed for image alignment, this work proposes a spatial transformer module that is used specifically for attention, as an alternative to the standard object detection models that typically precede the classifier to crop out the region of interest. In sum, the proposed end-to-end architecture dynamically scales and aligns the input images to maximize the classifier's performance, by selecting the thorax with translation and non-isotropic scaling transformations, and thus eliminating artifacts. Additionally, this paper provides an extensive and objective analysis of the selected regions of interest, by proposing a set of mathematical evaluation metrics. The results indicate that the STERN achieves similar results to using YOLO-cropped images, with reduced computational cost and without the need for localization labels. More specifically, the system is able to distinguish abnormal frontal images from the CheXpert dataset, with a mean AUC of 85.67% -a 2.55% improvement vs. the 0.98% improvement achieved by the YOLO-based counterpart in comparison to a standard baseline classifier. At the same time, the STERN approach requires less than 2/3 of the training parameters, while increasing the inference time per batch in less than 2 ms. Code available via GitHub.

Abstract
Chest radiography has been widely used for automatic analysis through deep learning (DL) techniques. However, in the manual analysis of these scans, comparison with images at previous time points is commonly done, in order to establish a longitudinal reference. The usage of longitudinal information in automatic analysis is not a common practice, but it might provide relevant information for desired output. In this work, the application of longitudinal information for the detection of cardiomegaly and change in pairs of CXR images was studied. Multiple experiments were performed, where the inclusion of longitudinal information was done at the features level and at the input level. The impact of the alignment of the image pairs (through a developed method) was also studied. The usage of aligned images was revealed to improve the final mcs for both the detection of pathology and change, in comparison to a standard multi-label classifier baseline. The model that uses concatenated image features outperformed the remaining, with an Area Under the Receiver Operating Characteristics Curve (AUC) of 0.858 for change detection, and presenting an AUC of 0.897 for the detection of pathology, showing that pathology features can be used to predict more efficiently the comparison between images. In order to further improve the developed methods, data augmentation techniques were studied. These proved that increasing the representation of minority classes leads to higher noise in the dataset. It also showed that neglecting the temporal order of the images can be an advantageous augmentation technique in longitudinal change studies.

Abstract
When designing clinical trials for testing novel cardiovascular therapies, it is highly relevant to understand what a given technology can provide in terms of information on the physiologic status of the heart and vessels. Ultrasound imaging has traditionally been the modality of choice to study the cardiovascular system as it has an excellent temporal resolution; it operates in real-time; it is very widespread and - not unimportant - it is cheap. Although this modality is mostly known clinically as a two-dimensional technology, it has recently matured into a true three-dimensional imaging technique. In this review paper, an overview is given of the available ultrasound technology for cardiac chamber quantification in terms of volume and function and evidence is given why these parameters are of value when testing the effect of new cardiovascular therapies.

Abstract
Chest radiography is one of the most ubiquitous medical imaging modalities. Nevertheless, the interpretation of chest radiography images is time-consuming, complex and subject to observer variability. As such, automated diagnosis systems for pathology detection have been proposed, aiming to reduce the burden on radiologists. The advent of deep learning has fostered the development of solutions for both abnormality detection with promising results. However, these tools suffer from poor explainability as the reasons that led to a decision cannot be easily understood, representing a major hurdle for their adoption in clinical practice. In order to overcome this issue, a method for chest radiography abnormality detection is presented which relies on an object detection framework to detect individual findings and thus separate normal and abnormal CXRs. It is shown that this framework is capable of an excellent performance in abnormality detection (AUC: 0.993), outperforming other state-of-the-art classification methodologies (AUC: 0.976 using the same classes). Furthermore, validation on external datasets shows that the proposed framework has a smaller drop in performance when applied to previously unseen data (21.9% vs 23.4% on average). Several approaches for object detection are compared and it is shown that merging pathology classes to minimize radiologist variability improves the localization of abnormal regions (0.529 vs 0.491 APF when using all pathology classes), resulting in a network which is more explainable and thus more suitable for integration in clinical practice.

Abstract
Two-dimensional echocardiography is the most widely used non-invasive imaging modality due to its fast acquisition time, low cost, and high temporal resolution. Accurate segmentation of the left ventricle in echocardiography is vital for ensuring the accuracy of subsequent diagnosis. Currently, numerous efforts have been made to automatize this task and various public datasets have been released in recent decades to further develop present research. However, medical datasets acquired at different institutions have inherent bias caused by various confounding factors, such as operation policies, machine protocols, treatment preference, etc. As a result, models trained on one dataset, regardless of volume, cannot be confidently utilized for the others. In this study, we investigated model robustness to dataset bias using two publicly available echocardiographic datasets. This work validates the efficacy of a supervised deep learning model for left ventricle segmentation and ejection fraction prediction, outside the dataset on which it was trained. The exposure of this model to unseen, but related samples without additional training maintained a good performance. However, a performance decrease from the original results can be observed, while the impact of quality is also noteworthy with lower quality data leading to decreased performance.