5 papers
CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification
Mikhail Goncharov, Maxim Pisov, Alexey Shevtsov +8
The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody…
Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification
Maxim Pisov, Vladimir Kondratenko, Alexey Zakharov +4
Vertebral body compression fractures are reliable early signs of osteoporosis. Though these fractures are visible on Computed Tomography (CT) images, they are frequently missed by…
Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection
Maxim Pisov, Mikhail Goncharov, Nadezhda Kurochkina +9
Midline shift (MLS) is a well-established factor used for outcome prediction in traumatic brain injury, stroke and brain tumors. The importance of automatic estimation of MLS was r…
Brain Tumor Image Retrieval via Multitask Learning
Maxim Pisov, Gleb Makarchuk, Valery Kostjuchenko +3
Classification-based image retrieval systems are built by training convolutional neural networks (CNNs) on a relevant classification problem and using the distance in the resulting…
Ensembling Neural Networks for Digital Pathology Images Classification and Segmentation
Gleb Makarchuk, Vladimir Kondratenko, Maxim Pisov +3
In the last years, neural networks have proven to be a powerful framework for various image analysis problems. However, some application domains have specific limitations. Notably,…