Medical Imaging and Machine Learning

  title={Medical Imaging and Machine Learning},
  author={Rohan Shad and John P. Cunningham and Euan A. Ashley and C. Langlotz and William Hiesinger},
Advances in computing power, deep learning architectures, and expert labelled datasets have spurred the development of medical imaging artificial intelligence systems that rival clinical experts in a variety of scenarios. The National Institutes of Health in 2018 identified key focus areas for the future of artificial intelligence in medical imaging, creating a foundational roadmap for research in image acquisition, algorithms, data standardization, and translatable clinical decision support… 
2 Citations

Deep Learning Approaches to Automatic Chronic Venous Disease Classification

This work uses deep learning methods for automatic classification of the stage of CVD for self-diagnosis of a patient by using the image of the patient’s legs to solve the binary classification problem “legs–no legs” and multi-classification problem according to the CEAP classification.

A review of explainable and interpretable AI with applications in COVID‐19 imaging

This review will allow developers of AI systems for COVID‐19 to quickly understand the basics of several explainable AI techniques and assist in the selection of an approach that is both appropriate and effective for a given scenario.



Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality.

It is shown that a convolutional neural network trained on raw pixel data in 812,278 echocardiographic videos from 34,362 individuals provides superior predictions of one-year all-cause mortality.

Deep Learning Prediction of Biomarkers from Echocardiogram Videos

EchoNet-Labs, a video-based deep learning algorithm to predict anemia, elevated B-type natriuretic peptide (BNP), troponin I, and blood urea nitrogen (BUN), and abnormal levels in ten additional lab tests, shows that deep learning applied to diagnostic imaging can provide additional clinical value and identify phenotypic information beyond current imaging interpretation methods.

Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images

A deep learning-based system (HCC-SurvNet) that provides risk scores for disease recurrence after primary resection, directly from hematoxylin and eosin-stained digital whole-slide images of formalin-fixed, paraffin embedded liver resections is developed and validated.

Deep learning-enabled medical computer vision

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 is surveyed.

Ethical Machine Learning in Health

The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML

Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines

Different data fusion techniques that can be applied to combine medical imaging with EHR, and systematically review medical data fusion literature published between 2012 and 2020 are described.

Ethical Machine Learning in Health Care

Ethics of ML in healthcare is frame through the lens of social justice, and ongoing efforts and challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations are described.

High performance on-demand de-identification of a petabyte-scale medical imaging data lake

A flexible solution for on-demand de-identification that combines the use of mature software technologies with modern cloud-based distributed computing techniques to enable faster turnaround in medical imaging research.