A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT

In a worldwide health crisis as exigent as COVID-19, there has become a pressing need for rapid, reliable diagnostics. Currently, popular testing methods such as reverse transcription polymerase chain reaction (RT-PCR) can have high false negative rates. Consequently, COVID-19 patients are not accurately identified nor treated quickly enough to prevent transmission of the virus. However, the recent rise of medical CT data has presented promising avenues, since CT manifestations contain key characteristics indicative of COVID-19. This study aimed to take a novel approach in the machine learning-based detection of COVID-19 from chest CT scans. First, the dataset utilized in this study was derived from three major sources, comprising a total of 17,698 chest CT slices across 923 patient cases. Image preprocessing algorithms were then developed to reduce noise by excluding irrelevant features. Transfer learning was also implemented with the EfficientNetB7 pre-trained model to provide a backbone architecture and save computational resources. Lastly, several explainability techniques were leveraged to qualitatively validate model performance by localizing infected regions and highlighting fine-grained pixel details. The proposed model attained an overall accuracy of 0.927 and a sensitivity of 0.958. Explainability measures showed that the model correctly distinguished between relevant, critical features pertaining to COVID-19 chest CT images and normal controls. Deep learning frameworks provide efficient, human-interpretable COVID-19 diagnostics that could complement radiologist decisions or serve as an alternative screening tool. Future endeavors may provide insight into infection severity, patient risk stratification, and prognosis.Comment: 19 page

The Boston University Photonics Center annual report 2016-2017

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2016-2017 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has undoubtedly been the Photonics Center’s best year since I became Director 10 years ago. In the following pages, you will see highlights of the Center’s activities in the past year, including more than 100 notable scholarly publications in the leading journals in our field, and the attraction of more than 22 million dollars in new research grants/contracts. Last year I had the honor to lead an international search for the first recipient of the Moustakas Endowed Professorship in Optics and Photonics, in collaboration with ECE Department Chair Clem Karl. This professorship honors the Center’s most impactful scholar and one of the Center’s founding visionaries, Professor Theodore Moustakas. We are delighted to haveawarded this professorship to Professor Ji-Xin Cheng, who joined our faculty this year.The past year also marked the launch of Boston University’s Neurophotonics Center, which will be allied closely with the Photonics Center. Leading that Center will be a distinguished new faculty member, Professor David Boas. David and I are together leading a new Neurophotonics NSF Research Traineeship Program that will provide $3M to promote graduate traineeships in this emerging new field. We had a busy summer hosting NSF Sites for Research Experiences for Undergraduates, Research Experiences for Teachers, and the BU Student Satellite Program. As a community, we emphasized the theme of “Optics of Cancer Imaging” at our annual symposium, hosted by Darren Roblyer. We entered a five-year second phase of NSF funding in our Industry/University Collaborative Research Center on Biophotonic Sensors and Systems, which has become the centerpiece of our translational biophotonics program. That I/UCRC continues to focus on advancing the health care and medical device industries

Disrupting 3D printing of medicines with machine learning.

3D printing (3DP) is a progressive technology capable of transforming pharmaceutical development. However, despite its promising advantages, its transition into clinical settings remains slow. To make the vital leap to mainstream clinical practice and improve patient care, 3DP must harness modern technologies. Machine learning (ML), an influential branch of artificial intelligence, may be a key partner for 3DP. Together, 3DP and ML can utilise intelligence based on human learning to accelerate drug product development, ensure stringent quality control (QC), and inspire innovative dosage-form design. With ML's capabilities, streamlined 3DP drug delivery could mark the next era of personalised medicine. This review details how ML can be applied to elevate the 3DP of pharmaceuticals and importantly, how it can expedite 3DP's integration into mainstream healthcare

AtomLbs: An Atom Based Convolutional Neural Network for Druggable Ligand Binding Site Prediction

Despite advances in drug research and development, there are few and ineffective treatments for a variety of diseases. Virtual screening can drastically reduce costs and accelerate the drug discovery process. Binding site identification is one of the initial and most important steps in structure-based virtual screening. Identifying and defining protein cavities that are likely to bind to a small compound is the objective of this task. In this research, we propose four different convolutional neural networks for predicting ligand-binding sites in proteins. A parallel optimized data pipeline is created to enable faster training of these neural network models on minimal hardware. The effectiveness of each method is assessed on well-established ligand binding site datasets. It is then compared with the state-of-the-art and widely used methods for ligand binding site identification. The result shows that our methods outperform most of the other methods and are comparable to the state-of-the-art methods

Digital Pathology: The Time Is Now to Bridge the Gap between Medicine and Technological Singularity

Digitalization of the imaging in radiology is a reality in several healthcare institutions worldwide. The challenges of filing, confidentiality, and manipulation have been brilliantly solved in radiology. However, digitalization of hematoxylin- and eosin-stained routine histological slides has shown slow movement. Although the application for external quality assurance is a reality for a pathologist with most of the continuing medical education programs utilizing virtual microscopy, the abandonment of traditional glass slides for routine diagnostics is far from the perspectives of many departments of laboratory medicine and pathology. Digital pathology images are captured as images by scanning and whole slide imaging/virtual microscopy can be obtained by microscopy (robotic) on an entire histological (microscopic) glass slide. Since 1986, services using telepathology for the transfer of images of anatomic pathology between detached locations have benefited countless patients globally, including the University of Alberta. The purpose of specialist recertification or re-validation for the Royal College of Pathologists of Canada belonging to the Royal College of Physicians and Surgeons of Canada and College of American Pathologists is a milestone in virtual reality. Challenges, such as high bandwidth requirement, electronic platforms, the stability of the operating systems, have been targeted and are improving enormously. The encryption of digital images may be a requirement for the accreditation of laboratory services—quantum computing results in quantum-mechanical phenomena, such as superposition and entanglement. Different from binary digital electronic computers based on transistors where data are encoded into binary digits (bits) with two different states (0 and 1), quantum computing uses quantum bits (qubits), which can be in superpositions of states. The use of quantum computing protocols on encrypted data is crucial for the permanent implementation of virtual pathology in hospitals and universities. Quantum computing may well represent the technological singularity to create new classifications and taxonomic rules in medicine

Chest infection classification from X-ray images using enhanced multisource transfer learning with voting system

Chest infection is a major health threat in most regions of the world. It is claimed to be one of the top causes of postoperative death after fragility hip fractures, according to a study presented in 2011. With the invention of deep learning in machine learning, implementation in Computer Aided Diagnosis system which utilizes deep neural networks for learning, classification, generation and even clustering has allowed X-ray image classification to be more accurate. The improvement in medical image classification using transfer learning is further studied. In this thesis, a novel deep neural network model which is composed of two Convolutional Neural Networks (CNNs) with different depth of weight layers, where the prediction probabilities for all CNNs are fused to the voting system for chest X-ray image classification is proposed and presented. The performance and accuracy of several existing deep learning model are investigated and compared to the proposed model. The outcome of this work, we successfully classified chest infection in chest X-ray images using the proposed model with overall accuracy of 83.69%

ICR ANNUAL REPORT 2020 (Volume 27)[All Pages]

This Annual Report covers from 1 January to 31 December 202

A community challenge for a pancancer drug mechanism of action inference from perturbational profile data

The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with similar to 400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among similar to 1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action.Peer reviewe

3D Stem Cell Culture

Recently, stem cells have been drawing increasing interest in basic and translational research that aims to understand stem cell biology and generate new therapies for various disorders. Many stem cells can be cultured in 2D relatively easily using tissue culture plastic. However, many of these cultures do not represent the natural conditions of stem cells in the body. In the body, microenvironments include numerous supporting cells and molecules. Therefore, researchers and clinicians have sought ideal stem cell preparations for basic research and clinical applications, which may be attainable through 3D culture of stem cells. The 3D cultures mimic the conditions of the natural environment of stem cells better, as cells in 3D cultures exhibit many unique and desirable characteristics that could be beneficial for therapeutic interventions. 3D stem cell cultures may employ supporting structures, such as various matrices or scaffolds, in addition to stem cells, to support complex structures. This book brings together recent research on 3D cultures of various stem cells to increase the basic understanding of stem cell culture techniques and also to highlight stem cell preparations for possible novel therapeutic applications

ICR ANNUAL REPORT 2019 (Volume 26)[All Pages]

This Annual Report covers from 1 January to 31 December 201