Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.Comment: 13 Figures, 5 tables. arXiv admin note: text overlap with arXiv:2003.1314

Deep multiple-instance learning for detecting multiple myeloma in CT scans of large bones

S nástupem moderních algoritmů strojového učení vzrostla popularita tématu automatické interpretace výstupů zobrazovacích metod v medicíně pomocí počítačů. Konvoluční neuronové sítě v současné době excelují v mnoha oblastech strojového vidění včetně rozpoznávání obrazu. V této diplomové práci zkoumáme možnosti využití konvolučních sítí jako diagnostického nástroje pro detekci abnormalit v CT snímcích stehenních kostí. Zaměřujeme se na diagnózu mnohočetného myelomu pro nějž jsou charakteristické viditelné léze v kostní dřeni, které lze pozorovat při vyšetření pomocí počítačové tomografie. Bylo otestováno několik různých přístupů včetně učení z více instancí. Náš klasifikátor podává spolehlivý výkon v experimentech s plně supervizovaným učením, vykazuje ovšem zásadní neschopnost konvergence při učení z více instancí. Předpokládáme, že náš navrhovaný neuronový model potřebuje ke konvergenci silnější chybovou odezvu a na toto téma navrhujeme budoucí možná vylepšení.The employment of computer aided diagnosis (CAD) systems for interpretation of medical images has become an increasingly popular topic with the arrival of modern machine learning algorithms. Convolutional neural networks perform exceptionally well nowadays in various pattern recognition tasks including image classification. In this thesis we examine the capabilities of a convolutional neural network binary classifier as a CAD system for detection of abnormalities in CT images of femurs. We focus on the diagnosis of multiple myeloma characterized by symptomatic bone marrow lesions commonly observable through computer tomography screening. Different approaches to the problem including multiple instance learning (MIL) were tested. The classifier showed a solid performance in our fully supervised experimental setting, it however exhibits a serious inability to learn from multiple instances. We conclude that the proposed neural model needs a stronger error signal in order to converge in the standard MIL setting and suggest potential improvements for further work in this area

The Effectiveness of Transfer Learning Systems on Medical Images

Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. Current deep learning (DL) algorithms, specifically convolutional neural networks are increasingly becoming the methodological choice for most medical image analysis. However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned from related data to solve a new problem. The objective of this dissertation is to examine the effectiveness of TL systems on medical images. First, a comprehensive systematic literature review was performed to provide an up-to-date status of TL systems on medical images. Specifically, we proposed a novel conceptual framework to organize the review. Second, a novel DL network was pretrained on natural images and utilized to evaluate the effectiveness of TL on a very large medical image dataset, specifically Chest X-rays images. Lastly, domain adaptation using an autoencoder was evaluated on the medical image dataset and the results confirmed the effectiveness of TL through fine-tuning strategies. We make several contributions to TL systems on medical image analysis: Firstly, we present a novel survey of TL on medical images and propose a new conceptual framework to organize the findings. Secondly, we propose a novel DL architecture to improve learned representations of medical images while mitigating the problem of vanishing gradients. Additionally, we identified the optimal cut-off layer (OCL) that provided the best model performance. We found that the higher layers in the proposed deep model give a better feature representation of our medical image task. Finally, we analyzed the effect of domain adaptation by fine-tuning an autoencoder on our medical images and provide theoretical contributions on the application of the transductive TL approach. The contributions herein reveal several research gaps to motivate future research and contribute to the body of literature in this active research area of TL systems on medical image analysis

Attention-Enhanced Cross-Task Network for Analysing Multiple Attributes of Lung Nodules in CT

Accurate characterisation of visual attributes such as spiculation, lobulation, and calcification of lung nodules is critical in cancer management. The characterisation of these attributes is often subjective, which may lead to high inter- and intra-observer variability. Furthermore, lung nodules are often heterogeneous in the cross-sectional image slices of a 3D volume. Current state-of-the-art methods that score multiple attributes rely on deep learning-based multi-task learning (MTL) schemes. These methods, however, extract shared visual features across attributes and then examine each attribute without explicitly leveraging their inherent intercorrelations. Furthermore, current methods either treat each slice with equal importance without considering their relevance or heterogeneity, which limits performance. In this study, we address these challenges with a new convolutional neural network (CNN)-based MTL model that incorporates multiple attention-based learning modules to simultaneously score 9 visual attributes of lung nodules in computed tomography (CT) image volumes. Our model processes entire nodule volumes of arbitrary depth and uses a slice attention module to filter out irrelevant slices. We also introduce cross-attribute and attribute specialisation attention modules that learn an optimal amalgamation of meaningful representations to leverage relationships between attributes. We demonstrate that our model outperforms previous state-of-the-art methods at scoring attributes using the well-known public LIDC-IDRI dataset of pulmonary nodules from over 1,000 patients. Our model also performs competitively when repurposed for benign-malignant classification. Our attention modules also provide easy-to-interpret weights that offer insights into the predictions of the model

A survey of deep learning models in medical therapeutic areas.

pre-print1407 K

Bioinformatic analysis and deep learning on large-scale human transcriptomic data: studies on aging, Alzheimer’s neurodegeneration and cancer

[ES] El objetivo general del proyecto ha sido el análisis bioinformático integrativo de datos múltiples de proteómica y genómica combinados con datos clínicos asociados para la búsqueda de biomarcadores y módulos poligénicos causales aplicado a enfermedades complejas; principalmente, cáncer de origen primario desconocido, en sus distintos tipos y subtipos y enfermedades neurodegenerativas (ND) mayormente Alzheimer, además de neurodegeneración debida a la edad. Además, se ha hecho un uso intensivo de técnicas de inteligencia artificial, más en concreto de técnicas de redes neuronales de aprendizaje profundo para el análisis y pronóstico de dichas enfermedades