The Laboratory is committed to technological innovation, and the team is dedicated to developing advanced neural networks, inspired by the latest advances in Neuroscience. We aim to apply these cutting-edge technologies to address complex challenges in various application fields, with a particular focus on the medical area.
In our laboratory, we combine theory and practice to design solutions that can radically transform the way medical diagnoses are performed. We use innovative neural networks to analyze and interpret a wide range of biomedical data, improving the accuracy and efficiency of diagnoses across different organs and types of scans.
We are committed to ongoing research and development of methodologies that address current medical needs and anticipate future directions in medical technology. Explore our site to learn more about our work, our publications, and how our research is helping to improve people’s health and well-being.
THE TEAM:
Alessio Fagioli
Marco Cascio
Luigi Cinque
PUBLICATIONS:
– Danilo Avola, Irene Cannistraci, Marco Cascio, Luigi Cinque, Alessio Fagioli, Gian Luca Foresti, Emanuele Rodolà, and Luciana Solito. “MV MS FETE: Multi view multi scale feature extractor and transformer encoder for stenosis recognition in echocardiograms”. In: Computer Methods and Programs in Biomedicine 245 (2024), p. 108037.
– Alessio Fagioli, Danilo Avola, Luigi Cinque, Emanuela Colombi, and Gian Luca Foresti. “Writer Identification in Historical Handwritten Documents: A Latin Dataset and a Benchmark”. In: International Conference on Image Analysis and Processing. Springer. 2023, pp. 465–476.
– Danilo Avola, Andrea Bacciu, Luigi Cinque, Alessio Fagioli, Marco Raoul Marini, and Riccardo Taiello. “Study on transfer learning capabilities for pneumonia classification in chest x rays images”. In: Computer Methods and Programs in Biomedicine 221 (2022), p. 106833.
– Danilo Avola, Luigi Cinque, Alessio Fagioli, and Gian Luca Foresti. “SIRe Networks: Convolutional neural networks architectural extension for information preservation via skip/residual connections and interlaced auto encoders”. In: Neural Networks 153 (2022), pp. 386–39.
– Danilo Avola, Luigi Cinque, Anxhelo Diko, Alessio Fagioli, Gian Luca Foresti, Alessio Mecca, Daniele Pannone, and Claudio Piciarelli. “MS Faster R CNN: Multi stream backbone for improved Faster R CNN object detection and aerial tracking from UAV images”. In: Remote Sensing 13.9 (2021), p. 1670
– Danilo Avola, Luigi Cinque, Alessio Fagioli, Sebastiano Filetti, Giorgio Grani, and Emanuele Rodolà. “Multimodal feature fusion and knowledge driven learning via experts consult for thyroid nodule classification”. In: IEEE Transactions on Circuits and Systems for Video Technology 32.5 (2021), pp. 2527–253-
– Danilo Avola, Luigi Cinque, Alessio Fagioli, Gianluca Foresti, and Alessio Mecca. “Ultrasound medical imaging techniques: a survey”. In: ACM Computing Surveys (CSUR) 54.3 (2021), pp. 1–38.