Dorin Comaniciu

TL;DR: In this article , the authors proposed a method to learn from medical images at scale in a self-supervised way based on contrastive learning and online feature clustering, which leverages training datasets of over 100,000,000 medical images of various modalities, including radiography, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasonography (US).

TL;DR: In this paper, a confidence of the detection may be used to indicate confidence of calculated quantities and/or to place the sampling planes or flow regions spaced from the valve and based on multiple valves.

TL;DR: In this chapter, the constrained MSL is presented to exploit correlations learned from the training set to increase the efficiency of the computational framework and employ the quaternion formulation for 3D orientation representation and distance measurement.

TL;DR: This chapter presents an automatic object detection and segmentation framework based on Marginal Space Learning (MSL), which integrates the components described in previous chapters into a complete segmentation system.

TL;DR: Severe COVID-19 can be predicted using only age and quantitative AD imaging metrics at initial diagnosis, which outperform the set of CVs, and AI-read qCXR can replace qCT metrics without loss of prognostic performance, promising more resource-efficient prognostication.