Publications by authors named "G Chabin"
Article Synopsis
- A significant increase in focal liver lesions (FLLs) detected by imaging emphasizes the need for an effective automated detection system using AI technology.
- In a study involving 395 patients and 1149 FLLs, researchers analyzed the effectiveness of AI software in identifying and measuring lesions through various MRI techniques.
- The collaborative use of AI and radiologists improved detection rates, with AI showing greater sensitivity for smaller lesions (<20 mm), and both achieving excellent performance for larger lesions (≥20 mm).
Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. However, medical image datasets are often low in sample size and only partially labeled, i.e.
View Article and Find Full Text PDFBackground: We describe and evaluate a deep network algorithm which automatically contours organs at risk in the thorax and pelvis on computed tomography (CT) images for radiation treatment planning.
Methods: The algorithm identifies the region of interest (ROI) automatically by detecting anatomical landmarks around the specific organs using a deep reinforcement learning technique. The segmentation is restricted to this ROI and performed by a deep image-to-image network (DI2IN) based on a convolutional encoder-decoder architecture combined with multi-level feature concatenation.
Objectives: To investigate machine learning classifiers and interpretable models using chest CT for detection of COVID-19 and differentiation from other pneumonias, interstitial lung disease (ILD) and normal CTs.
Methods: Our retrospective multi-institutional study obtained 2446 chest CTs from 16 institutions (including 1161 COVID-19 patients). Training/validation/testing cohorts included 1011/50/100 COVID-19, 388/16/33 ILD, 189/16/33 other pneumonias, and 559/17/34 normal (no pathologies) CTs.