Papers
2
Total Citations
24
H-Index
2
About
Fabio Scotti is a researcher whose work bridges the fields of artificial intelligence and medical surgery, with a particular focus on facial expression recognition and head and neck cancer treatment. His key research areas include weakly supervised learning, active incremental learning, and transoral surgical techniques. Scotti’s major contribution is the development of a transferred Deep Active Learning Convolutional Neural Network (DAL-CNN) for facial expression recognition, which innovatively combines transfer learning with active incremental learning to improve accuracy with limited labeled data—a significant advancement in affective computing. This work has garnered 18 citations, reflecting its impact on computer vision and human-computer interaction. Additionally, Scotti co-authored a notable clinical study analyzing nine years of transoral laser microsurgery (TLM) data to assess patient suitability for transoral robotic surgery (TORS), providing crucial insights for surgical planning and caseload estimation in head and neck oncology. This dual expertise in computational methods and surgical applications highlights Scotti’s unique interdisciplinary approach, making his research valuable for both AI practitioners and medical professionals seeking to integrate intelligent systems into clinical practice.
Research Focus
Key Achievements
Top Papers
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