NEW PUBLICATION – First research results of the BIOCHIP (Intelligent BIOsensors based on CHImeric Proteins) project have been published by M.O.D.A.L. on the top-ranking journal Biosensors and Bioelectronics (Elsevier).

The project “Intelligent biosensors based on chimeric proteins” (BIOCHIP) will synergically integrate principles and potentialities derived from biochemistry, chemistry, and informatics, to create intelligent biosensors endowed with high sensitivity, specificity, reliability, and ability to work in a wide range of matrices.

Link to the publication:

https://www.sciencedirect.com/science/article/pii/S0956566321007338

Link to the UNINA news:

http://www.unina.it/-/27081936-progetto-di-ricerca-biochip-pubblicati-i-primi-risultati-su-prestigiosa-rivista

We are proud to announce that the paper “Predictive Medicine for Salivary Gland Tumours Identification Through Deep Learning” has been published in the Journal of Biomedical and Health Informatics. 🎉  This research provides a promising application of AI in predictive medicine and can pave the way for the development of more accurate and personalized medical treatments. 💻📈
Check out more at https://ieeexplore.ieee.org/abstract/document/9573315?casa_token=J1rtYgM-ugcAAAAA:U19s6LI6xNl9zoIUoXDTOm7ytwudAUGDk8_XmHodORg0fcRzc_gd1MtEDR4xhyyP_hiV1hQ

This research work presents and discusses a Deep Learning-based framework for automatic segmentation and classification of salivary gland tumours. Furthermore, we propose an explainable segmentation learning approach supporting the effectiveness of the proposed framework through a per-epoch learning process analysis and the attention map mechanism. The proposed framework was evaluated with a collected CT dataset of patients with salivary gland tumours. Experimental results show that our methodology achieves significant scores on both segmentation and classification tasks.

Link to Publication:

https://ieeexplore.ieee.org/abstract/document/9573315