1. A convolutional neural network with transfer learning for automatic discrimination between low and high-grade synovitis: a pilot study
- Author
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Giuseppe Lopalco, Eugenio Maiorano, Gerardo Cazzato, Antonietta Cimmino, Orazio Angelini, Vincenzo Venerito, and Florenzo Iannone
- Subjects
medicine.medical_specialty ,medicine.diagnostic_test ,business.industry ,H&E stain ,030204 cardiovascular system & hematology ,medicine.disease ,Convolutional neural network ,Patient management ,03 medical and health sciences ,0302 clinical medicine ,Synovitis ,Biopsy ,Emergency Medicine ,Internal Medicine ,Medicine ,030212 general & internal medicine ,Test phase ,Radiology ,business ,Transfer of learning ,Tissue inflammation - Abstract
Ultrasound-guided synovial tissue biopsy (USSB) may allow personalizing the treatment for patients with inflammatory arthritis. To this end, the quantification of tissue inflammation in synovial specimens can be crucial to adopt proper therapeutic strategies. This study aimed at investigating whether computer vision may be of aid in discriminating the grade of synovitis in patients undergoing USSB. We used a database of 150 photomicrographs of synovium from patients who underwent USSB. For each hematoxylin and eosin (H&E)-stained slide, Krenn’s score was calculated. After proper data pre-processing and fine-tuning, transfer learning on a ResNet34 convolutional neural network (CNN) was employed to discriminate between low and high-grade synovitis (Krenn’s score
- Published
- 2021