European archives of psychiatry and clinical neuroscience

A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields-including medicine-to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques-powered by deep learning-for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit-including cardiology, pathology, dermatology, ophthalmology-and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

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Deep learning-enabled medical computer vision.

This chapter contains sections titled: Learning Machine Statistical Learning Theory Types of Learning Methods Common Learning Tasks Model Estimation Review Questions and Problems References for further study

Learning from Data

The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.

/pdf/3d-deep-learning-on-medical-images-a-review-3qmqxg4rtd.pdf

3D Deep Learning on Medical Images: A Review.

Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud. However, FL is difficult to implement and deploy in practice, considering the heterogeneity in mobile devices, e.g., different programming languages, frameworks, and hardware accelerators. Although there are a few frameworks available to simulate FL algorithms (e.g., TensorFlow Federated), they do not support implementing FL workloads on mobile devices. Furthermore, these frameworks are designed to simulate FL in a server environment and hence do not allow experimentation in distributed mobile settings for a large number of clients. In this paper, we present Flower (https://flower.dev/), a FL framework which is both agnostic towards heterogeneous client environments and also scales to a large number of clients, including mobile and embedded devices. Flower's abstractions let developers port existing mobile workloads with little overhead, regardless of the programming language or ML framework used, while also allowing researchers flexibility to experiment with novel approaches to advance the state-of-the-art. We describe the design goals and implementation considerations of Flower and show our experiences in evaluating the performance of FL across clients with heterogeneous computational and communication capabilities.

Flower: A Friendly Federated Learning Research Framework

Decoding human activity accurately from wearable sensors can aid in applications related to healthcare and context awareness. The present approaches in this domain use recurrent and/or convolutional models to capture the spatio-temporal features from time-series data from multiple sensors. We propose a deep neural network architecture that not only captures the spatio-temporal features of multiple sensor time-series data but also selects, learns important time points by utilizing a self-attention mechanism. We show the validity of the proposed approach across different data sampling strategies on six public datasets and demonstrate that the self-attention mechanism gave a significant improvement in performance over deep networks using a combination of recurrent and convolution networks. We also show that the proposed approach gave a statistically significant performance enhancement over previous state-of-the-art methods for the tested datasets. The proposed methods open avenues for better decoding of human activity from multiple body sensors over extended periods of time. The code implementation for the proposed model is available at https://github.com/isukrit/encodingHumanActivity

Deep ConvLSTM With Self-Attention for Human Activity Decoding Using Wearable Sensors

Successive layers in convolutional neural networks (CNN) extract different features from input images. Applications of CNNs to detect abnormalities in the 2D images or 3D volumes of body organs have recently become popular. However, computer-aided detection of diseases using deep CNN is challenging due to the absence of a large set of training medical images/scans and the relatively small and hard to detect abnormalities. In this paper, we propose a method for normalizing 3D volumetric scans using the intensity profile of the training samples. This aids the CNN by creating a higher contrast around the abnormal region of interest in the scan. We use the CQ500 head CT dataset to demonstrate the validity of our method for detecting different acute brain hemorrhages such as subarachnoid hemorrhage (SAH), intraparenchymal hemorrhage (IPH), subdural hematoma (SDH), and intraventricular hemorrhage (IVH). We compare the proposed method with a baseline, two variants of the 3D VGGNet architectures, Resnet, and show that the proposed method achieves significant improvement in classification performance. For binary classification, we achieved the best F1 score of 0.96 (normal vs SAH), 0.93 (normal vs IPH), 0.98 (normal vs SDH), and 0.99 (normal vs IVH), and for four-class classification, we obtained an average F1 score of 0.77. Finally, we show a limitation of the proposed method while detecting varied abnormalities. The proposed method has applications for abnormality detection for different organs.

Shallow 3D CNN for Detecting Acute Brain Hemorrhage From Medical Imaging Sensors

Betweenness centrality is a centrality measure that is widely used, with applications across several disciplines. It is a measure which quantifies the importance of a vertex based on its occurrence in shortest paths between all possible pairs of vertices in a graph. This is a global measure, and in order to find the betweenness centrality of a node, one is supposed to have complete information about the graph. Most of the algorithms that are used to find betwenness centrality assume the constancy of the graph and are not efficient for dynamic networks. We propose a technique to update betweenness centrality of a graph when nodes are added or deleted. Our algorithm experimentally speeds up the calculation of betweenness centrality (after updation) from 7 to 412 times, for real graphs, in comparison to the currently best known technique to find betweenness centrality.

https://www.internetmathematicsjournal.com/article/1584.pdf

Sukrit Gupta

Papers

3D Deep Learning on Medical Images: A Review.

Deep ConvLSTM With Self-Attention for Human Activity Decoding Using Wearable Sensors

Shallow 3D CNN for Detecting Acute Brain Hemorrhage From Medical Imaging Sensors

A Faster Algorithm to Update Betweenness Centrality After Node Alteration

3D Deep Learning on Medical Images: A Review