Enhancing Effeciency of Ejection Fraction Calculation in the Left Ventricle

The calculation of the cardiac ejection fraction is important for determining whether or not a patient suffers from cardiovascular disease. However, manual calculation of the ejection fraction (EF) is prone to errors and is known to be prohibitively time-consuming. As such, there have been endeavors to automate this process for the sake of saving time as well as improving accuracy of estimation. Recently,GPUhave been proposed to enhance the performance of machine learning algorithms that attempt to estimate the EF. In addition, these algorithms are considered a necessary component in solving computational efficiency issuesencountered in dealing with hugeDigital Imaging and Communications in Medicine (DICOM)datasets. In this study, we useda DICOM dataset of cardiac magnetic resonance imaging for 1200 human cases with different ages and gender to calculate the ejection fraction in the left ventricle.Convolutional Neural Network (CNN) was the selected neural network for the training phase of segmenting the LV and volume calculation. Our target is enhancing efficiencyof CNN to speedup training phase, and subsequently the prediction of the CVDs by experimenting with different GPU-based parallelism techniques, namely Data Parallelism (DP)and Model Parallelism (MP) in addition to the generic use of multiple GPUs. Specifically, we performed four variants of experiments; the first was using GPUs without applying any control on its behavior, the second two variants involve experiments using either DP alone or MP alone on multiple GPUs, while the fourth and final variant involves combining both DP and MP. This was done on Amazon EC2 instances that support up to 8 GPUs per instance. We used two EC2 instances to apply our experiment on 16 GPUs. Our experiments show that our proposed combination of both DP and MP havethe bestcomputational efficiency. Precisely, a speedup of up to 9.88 (over a single GPU) was achieved when using 16 GPUs in parallel with combined DP and MP

Kidney and Kidney-tumor Segmentation Using Cascaded V-Nets

Kidney cancer is the seventh most common cancer worldwide, accounting for an estimated 140,000 global deaths annually. Kidney segmentation in volumetric medical images plays an important role in clinical diagnosis, radiotherapy planning, interventional guidance and patient follow-ups however, to our knowledge, there is no automatic kidneytumor segmentation method present in the literature. In this paper, we address the challenge of simultaneous semantic segmentation of kidney and tumor by adopting a cascaded V-Net framework. The first V-Net in our pipeline produces a region of interest around the probable location of the kidney and tumor, which facilitates the removal of the unwanted region in the CT volume. The second sets of V-Nets are trained separately for the kidney and tumor, which produces the kidney and tumor masks respectively. The final segmentation is achieved by combining the kidney and tumor mask together. Our method is trained and validated on 190 and 20 patients scans, respectively, accesses from 2019 Kidney Tumor Segmentation Challenge database. We achieved a validation accuracy in terms of the Sørensen Dice coefficient of about 97%

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Análisis del funcionamiento cardíaco mediante redes neuronales

[ES] La detección de la disminución del funcionamiento cardíaco es un factor clave para el diagnóstico de enfermedades del corazón. Para analizarlo se obtiene el volumen del ventrículo izquierdo al final de la fase de sístole y diástole para estimar la sangre eyectada por el corazón, pero este proceso es lento y quita mucho tiempo a los médicos. En este trabajo se han implementado modelos basados en redes neuronales profundas para estimar el volumen del ventrículo izquierdo, buscando una buena generalización y aplicabilidad de estos para un caso de uso real. Para ello, además se han aplicado técnicas de model interpretability para analizar el comportamiento de los modelos y poder tomar más confianza sobre sus predicciones.[EN] Detection of declining heart function is a key factor in diagnosing heart disease. To analyze it, the volume of the left ventricle is obtained at the end of the systole and diastole phases to estimate the blood ejected by the heart, but this process is slow and takes a lot of time from the doctors. In this work, models based on deep neural networks have been implemented to estimate the volume of the left ventricle, seeking a good generalization and applicability of these for a real use case. For this, model interpretability techniques have also been applied to analyze the behavior of the models and to be able to gain more confidence about their predictions.[CA] La detecció de la disminució del funcionament cardíac és un factor clau per al diagnòstic de malalties del cor. Per a analitzar-ho s’obté el volum del ventricle esquerre al final de la fase de sístole i diàstole per a estimar la sang ejectada pel cor, però aquest procés és lent i lleva molt temps als metges. En aquest treball s’han implementat models basats en xarxes neuronals profundes per a estimar el volum del ventricle esquerre, buscant una bona generalització i aplicabilitat d’aquests per a un cas d’ús real. Per a això, a més s’han aplicat tècniques de model interpretability per a analitzar el comportament dels models i poder tindre més confiança sobre les seues prediccions.López Chilet, Á. (2020). Análisis del funcionamiento cardíaco mediante redes neuronales. Universitat Politècnica de València. http://hdl.handle.net/10251/151946TFG

Sparsely Activated Networks: A new method for decomposing and compressing data

Recent literature on unsupervised learning focused on designing structural priors with the aim of learning meaningful features, but without considering the description length of the representations. In this thesis, first we introduce the{\phi}metric that evaluates unsupervised models based on their reconstruction accuracy and the degree of compression of their internal representations. We then present and define two activation functions (Identity, ReLU) as base of reference and three sparse activation functions (top-k absolutes, Extrema-Pool indices, Extrema) as candidate structures that minimize the previously defined metric $\varphi$. We lastly present Sparsely Activated Networks (SANs) that consist of kernels with shared weights that, during encoding, are convolved with the input and then passed through a sparse activation function. During decoding, the same weights are convolved with the sparse activation map and subsequently the partial reconstructions from each weight are summed to reconstruct the input. We compare SANs using the five previously defined activation functions on a variety of datasets (Physionet, UCI-epilepsy, MNIST, FMNIST) and show that models that are selected using $\varphi$ have small description representation length and consist of interpretable kernels.Comment: PhD Thesis in Greek, 158 pages for the main text, 23 supplementary pages for presentation, arXiv:1907.06592, arXiv:1904.13216, arXiv:1902.1112