{"id":695,"date":"2021-10-14T19:25:38","date_gmt":"2021-10-14T17:25:38","guid":{"rendered":"http:\/\/www.labdma.unina.it\/?p=695"},"modified":"2023-02-26T19:09:22","modified_gmt":"2023-02-26T18:09:22","slug":"predictive-medicine-and-deep-learning","status":"publish","type":"post","link":"https:\/\/www.labdma.unina.it\/index.php\/2021\/10\/14\/predictive-medicine-and-deep-learning\/","title":{"rendered":"Predictive Medicine and Deep Learning"},"content":{"rendered":"\n

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. \ud83c\udf89  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. \ud83d\udcbb\ud83d\udcc8
Check out more at https:\/\/ieeexplore.ieee.org\/abstract\/document\/9573315?casa_token=J1rtYgM-ugcAAAAA:U19s6LI6xNl9zoIUoXDTOm7ytwudAUGDk8_XmHodORg0fcRzc_gd1MtEDR4xhyyP_hiV1hQ<\/a><\/p>\n\n\n\n

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.<\/p>\n\n\n\n

Link to Publication:<\/p>\n\n\n\n

https:\/\/ieeexplore.ieee.org\/abstract\/document\/9573315<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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<div class="slide-text-bg2">
\n<h3>We are proud to announce that the paper “Predictive Medicine for Salivary Gland Tumours Identification Through Deep Learning” has been<\/h3>
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