Data is the foundation of AI. Without well-annotated data, there is no AI. Especially in this deep learning era, the unreasonable effectiveness of data is well understood as the current generation of AI algorithms are fueled by massive public and private datasets [1]. While medical imaging data is widely available in clinics and hospitals around the world, the tools to create AI algorithms are not in the hands of physicians and domain experts.

Previously, we walked through the tools developed at SemanticMD to help medical imaging researchers manage and annotate data. In this tutorial, you will learn how to apply…

Deep learning web server and PACS running on Raspberry Pi

In honor of Pi Day (March 14), we are launching SemanticMD AI Box, our TB detection solution deployed on a Raspberry Pi. We sent our first one out today to Njaboute Foundation based in The Gambia and plan to send a 100 more out to TB clinics and NGOs around the world for evaluation. Contact us if you’re interested in a demo.

TB Detection in Chest X-rays

At SemanticMD, we’ve been developing a platform to facilitate the acquisition and curation of medical imaging and clinical data to develop artificial intelligence (AI) algorithms. As a team of machine learning scientists ourselves, we realized that curation of training data is often the rate limiting factor for developing new AI algorithms and image analysis techniques.

In order to solve the problem for medical imaging researchers, we developed a cloud-based platform and suite of software tools to facilitate the development of AI algorithms, including tools to redact images, annotate reports using NLP, crowd-source annotations from medical imaging specialists, and APIs for…

Today we have a guest post by Dr. Lance Reinsmith about blockchain for radiology. He is a self described Python and deep learning enthusiast. He lives in San Antonio, Texas and works as a diagnostic radiologist at South Texas Radiology Group.


A lot of progress has been made recently for the healthcare blockchain with companies like Optum, Humana, PokitDok and Hashed Health leading the way. However, most of the focus thus has been on securing and sharing financial transactions and electronic health records.

I believe blockchain technology could also play an integral role in storage and distribution of medical images…

When you think of analyzing massive and complex imaging data, the first challenge is to label the data according to some qualification of an expert. For example, you might download hundreds of X-rays from your imaging archive then label them in folders as normal and abnormal. This enables you to use these images to learn a model that captures those novel abnormalities. …

At SemanticMD we believe medical image analysis should be easy. Key to that is providing imaging scientists and researchers the tools to reduce data wrangling (see Data Janitor) and to scale machine learning algorithms beyond the typical limits of a script running on a single server. Our latest API release makes it easier than ever to scale your medical image analysis algorithms whether you are working on computer-aided diagnosis or detection. …

There are many companies providing machine learning solutions in healthcare. Some older companies like Microsoft and IBM, but also newer startups such as Enlitic, Lumiata, and Metamind. While these companies may use the latest technologies such as deep learning (i.e. caffe) and graph databases, the expert systems are still designed to instruct a doctor to see patterns learned by the expert system.

Real medical data analysis is messy and it’s rarely the case that a physician has a set of numerical and categorical features and they are just waiting for that perfect solution to map features to a clinical outcome…


We provide a deep learning platform for optimized analysis and interpretation of medical images

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