Deep learning in medical imaging

Developing models to detect heart disease and cancer.

Medical images that are captured from inside the body using various scanning and imaging techniques have traditionally been difficult and time consuming to analyse by trained experts. Well-performing deep learning models in the medical domain has the potential to assist doctors to increase certainty and streamline the analysis of such medical images.

In around 2015 the first papers on applications of deep learning on medical images were published. The largest interest at that time was for modalities like MRI, CT and microscopy, but less for the more low-cost and challenging modalities like X-ray and ultrasound. The first papers on deep learning in radiology and cancer screening appeared in 2016/2017. Current screening cases are typically related to detection, classification and segmentation of tumors in lungs.

In cardiac ultrasound, the first attempts using deep learning were published in 2016. Specific challenges are related to real-time inference, confidence estimation, limited training data, time series and combination of images and data sources.

The innovations in the field of medical imaging aim at obtaining more efficient tools for diagnosis support and decision support for diseases such as heart disease and cancer through the use of deep learning technologies. One of the major obstacles for this is the availability of training data within this area. Another very important aspect, when moving into diagnosis, is estimation of confidence and uncertainty in the predictions as wells as explanation of predictions. Furthermore, increased robustness to shifts and changes in shifts in sensors, parameters and cohorts is important.

Visual Intelligence is advancing deep learning in medicine and health to solve these challenges.

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Related projects

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Overcoming the challenges of limited training data in the medical domain and laying the fundamentals for explainability and reliability.
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Exploiting limited data to perform objective measurements in ultrasound images of the heart.