CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification

no code implementations • 2 Jun 2020 • Mikhail Goncharov, Maxim Pisov, Alexey Shevtsov, Boris Shirokikh, Anvar Kurmukov, Ivan Blokhin, Valeria Chernina, Alexander Solovev, Victor Gombolevskiy, Sergey Morozov, Mikhail Belyaev

We train our model on approximately 2000 publicly available CT studies and test it with a carefully designed set consisting of 32 COVID-19 studies, 30 cases with bacterial pneumonia, 31 healthy patients, and 30 patients with other lung pathologies to emulate a typical patient flow in an out-patient hospital.

Binary Classification

Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection

1 code implementation • 13 Aug 2019 • Maxim Pisov, Mikhail Goncharov, Nadezhda Kurochkina, Sergey Morozov, Victor Gombolevskiy, Valeria Chernina, Anton Vladzymyrskyy, Ksenia Zamyatina, Anna Chesnokova, Igor Pronin, Michael Shifrin, Mikhail Belyaev

Midline shift (MLS) is a well-established factor used for outcome prediction in traumatic brain injury, stroke and brain tumors.