"A Google Maps for surgeons" is how Perimeter Medical Imaging AI Inc. (TSXV: PINK) President and CFO Jeremy Sobotta described the AI software currently being developed by the company to complement its FDA-cleared medical imaging system at a recent investment conference. Perimeter is a medical technology company working to transform cancer surgery by creating ultra-high-resolution, real-time, advanced imaging tools to address unmet medical needs. The imaging tools have already been developed and are approved in ophthalmology and cardiology (optical coherence tomography or OCT). Perimeter is using this imaging technology (OTIS or Optical Tissue Imaging Console) to assess the tissues surrounding the known cancerous target area to determine whether more tissue should be removed during the ongoing surgery. The imaging technology has the ability to rapidly image large and complex surfaces.
With the development of medical imaging technology and machine learning, computer-assisted diagnosis which can provide impressive reference to pathologists, attracts extensive research interests. The exponential growth of medical images and uninterpretability of traditional classification models have hindered the applications of computer-assisted diagnosis. To address these issues, we propose a novel method for Learning Binary Semantic Embedding (LBSE). Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Furthermore, double supervision, bit uncorrelation and balance constraint, asymmetric strategy and discrete optimization are seamlessly integrated in the proposed method for learning binary embedding. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
The TriRhenaTech alliance presents a collection of accepted papers of the cancelled tri-national 'Upper-Rhine Artificial Inteeligence Symposium' planned for 13th May 2020 in Karlsruhe. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.
This is the first part in a multi-part series. I created this series, How I Built a Space to Train and Infer on Medical Imaging AI Models (HIBASTIMIAM) to share my process on how I built up an environment for medical imaging AI. I am writing this series during COVID-19 and the period of social distancing -- so I'm going to walk through doing this at home on my own gaming PC. While these steps could be applied generally to craft medical imaging AI models, specifically for this series, I'm going to walk through creating an AI model for classification of COVID-19. It's not going to be an amazing, robust accurate model by any stretch--this blog series is much more about how to get the pieces installed and working together.
Artificial intelligence (AI) is field of the computer science that allows machine to do interactive functions similar to humans. The AI allow systems to do activities such as speech recognition, learning, data monitoring, data recording and more. The use of AI technology in medical imaging allows to capture the part of the body and it visualizes the affected areas and assists in the treatments. Healthcare costs continue to rise, clinicians are overworked, and patient data privacy, security, and compliance are ongoing concerns. With limited budgets and shrinking margins, healthcare organizations must find new ways to improve operational efficiency while meeting--or exceeding--the highest standards of patient care.
Deep learning methods have proven extremely effective at performing a variety of medical image analysis tasks. With their potential use in clinical routine, their lack of transparency has however been one of their few weak points, raising concerns regarding their behavior and failure modes. While most research to infer model behavior has focused on indirect strategies that estimate prediction uncertainties and visualize model support in the input image space, the ability to explicitly query a prediction model regarding its image content offers a more direct way to determine the behavior of trained models. To this end, we present a novel Visual Question Answering approach that allows an image to be queried by means of a written question. Experiments on a variety of medical and natural image datasets show that by fusing image and question features in a novel way, the proposed approach achieves an equal or higher accuracy compared to current methods.
In this article, we will be looking at what is medical imaging, the different applications and use-cases of medical imaging, how artificial intelligence and deep learning is aiding the healthcare industry towards early and more accurate diagnosis. We will review literature about how machine learning is being applied in different spheres of medical imaging and in the end implement a binary classifier to diagnose diabetic retinopathy. Medical imaging consists of set of processes or techniques to create visual representations of the interior parts of the body such as organs or tissues for clinical purposes to monitor health, diagnose and treat diseases and injuries. Moreover, it also helps in creating database of anatomy and physiology. Owing to the advancements in the field today medical imaging has the ability to achieve information of human body for many useful clinical applications. Different types of medical imaging technology gives different information about the area of the body to be studied or medically treated. Organisations incorporating the medical imaging devices include freestanding radiology and pathology facilities as well as clinics and hospitals. Major manufacturers of these medical imaging devices include Fujifilm, GE, Siemens Healthineers, Philips, Toshiba, Hitachi and Samsung.
This article discusses how the language of causality can shed new light on the major challenges in machine learning for medical imaging: 1) data scarcity, which is the limited availability of high-quality annotations, and 2) data mismatch, whereby a trained algorithm may fail to generalize in clinical practice. Looking at these challenges through the lens of causality allows decisions about data collection, annotation procedures, and learning strategies to be made (and scrutinized) more transparently. We discuss how causal relationships between images and annotations can not only have profound effects on the performance of predictive models, but may even dictate which learning strategies should be considered in the first place. For example, we conclude that semi-supervision may be unsuitable for image segmentation---one of the possibly surprising insights from our causal analysis, which is illustrated with representative real-world examples of computer-aided diagnosis (skin lesion classification in dermatology) and radiotherapy (automated contouring of tumours). We highlight that being aware of and accounting for the causal relationships in medical imaging data is important for the safe development of machine learning and essential for regulation and responsible reporting. To facilitate this we provide step-by-step recommendations for future studies.
Medical imaging is among the most popular application of AI and machine learning, and with good reason. Computer vision algorithms are naturally adept at spotting anomalies experts sometimes miss, in the process reducing wait times and lightening clinical workloads. Perhaps that's why although the percentage of health care organizations that have adopted AI remains relatively low (22%) globally, the majority of practitioners (77%) believe the technology is important to the medical imaging field as a whole. Unsurprisingly, data scientists have devoted outsize time and attention to developing AI imaging models for use in health care systems, a few of which Google scientists detail in a paper accepted to this week's NeurIPS conference in Vancouver. In "Transfusion: Understanding Transfer Learning for Medical Imaging," coauthors hailing from Google Research (the R&D-focused arm of Google's business) investigate the role transfer learning plays in developing image classification algorithms.