Deep Learning in Healthcare – How it’s Changing the Game

deep learning in healthcare

Artificial intelligence (AI), machine learning, deep learning, semantic computing – these terms have been slowly permeating the medical industry for the past few years, bringing with them technology and solutions that are changing the shape of healthcare. Each of these technologies is connected, each one providing something different to the industry and changing how medical […]

Medical Imaging AI Glossary

Understanding what determines accuracy can be confusing. But it doesn’t have to be! To help you understand the most common terms used in artificial intelligence, machine learning, and statistics, we have composed a glossary of the most common terms you’ll hear in the Medical Imaging AI space. Accuracy The accuracy of a machine learning classification […]

Algorithms and AI: deep learning medical imaging

deep learning medical imaging

Artificial intelligence, neural networks and deep learning are the terms redefining medical proficiency and patient care in the healthcare industry Deep neural networks consist of multiple layers that allow for machines to sieve through vast quantities of data, solving complex problems and unlocking answers. It is the elegant evolution of AI capability and machine learning […]

How we reduced our AI algorithm training time from days to 3 hours

algorithm training in radiology

By using the most advanced data parallelization technologies, we reduced our Radiology AI algorithm training time from days to 3 hours Motivation At Aidoc, we use deep learning to detect abnormalities in radiology scans, helping doctors improve the standard of care in clinics and hospitals around the world. Deep learning is a highly empirical field, […]

A Proven Methodology for Building a Strong AI Team


Each week one of our AI team members dedicates a few days to learn novel concepts in deep learning or software engineering and present them to the team – we call these talks “Deep Snips”. We do this mainly by reading academic papers from leading groups, books and blog posts. We are not intimidated by […]

Overcoming AI Barriers in Health Care

AI has become increasingly critical for radiologists in the 21st century. Aidoc CEO Elad Walach describes in Forbes magazine how radiologists can utilize AI to bolster three keys areas in medical care: Safeguarding health data – AI harnesses data to provide a fuller picture of a patient’s medical condition and history. Furthermore, a fundamental understanding of AI […]

Wider Perspective on the Progress in Object Detection

Object Detection is one of the most mature fields in computer vision. In the last year alone we have seen many novel ideas in object detection that have introduced significant improvements in detection accuracy. I’ve gathered, in my opinion, the 9 most important and useful papers (since October 2016) for a talk I recently presented […]

Top-down and Bottom-Up Visual Attention


Visual attention mechanisms are known to be important components of modern computer vision systems and are an inherent part of state-of-the-art achievements in almost all fields: object detection, image-captioning, and more. Most conventional visual attention mechanisms use medical image captioning and VQA (visual question answering) from a Top-Down approach, a task-specific method that assigns captions […]

Elegant Automated Testing Solutions for Managing The Tsunami of Deep Learning Systems

Neural networks are now practically doing most of the coding instead of us (and better than us) in many fields, but we don’t have a satisfactory methodology for testing their behavior.A very interesting recent paper that was covered on “The Morning Paper” blog, shows a simple and elegant approach for testing neural networks. Basically, they […]

New Evidence Points to a Potentially Superior Deep Learning Project Management Strategy

A relatively recent paper by Baidu (Dec 2017) that was covered in The Morning Paper has empirically demonstrated something fascinating that could have major implications on the management of deep learning projects— Deep Learning output errors decrease predictably as a “power-law” of the training set size:  (m is the number of samples in the training set, and Beta is usually […]