{"id":346691,"date":"2022-01-15T12:00:00","date_gmt":"2022-01-15T11:00:00","guid":{"rendered":"https:\/\/innovationorigins.com\/?post_type=selected&p=346691"},"modified":"2022-01-15T12:00:00","modified_gmt":"2022-01-15T11:00:00","slug":"algorithm-classifies-skin-lesions","status":"publish","type":"selected","link":"https:\/\/ioplus.nl\/archive\/en\/selected\/algorithm-classifies-skin-lesions\/","title":{"rendered":"Algorithm classifies skin lesions"},"content":{"rendered":"\n

Many people worldwide suffer from skin diseases. For diagnosis, physicians often combine multiple information sources. These include, for instance, clinical images, microscopic images and meta-data such as the age and gender of the patient. Deep learning algorithms can support the classification of skin lesions by fusing all the information together and evaluating it. Several such algorithms are already being developed. However, to apply these learning algorithms in the clinic, they need to be further improved to achieve higher diagnostic accuracy, writes the Technical University of Munich in a press release<\/a>.<\/p>\n\n\n\n

New data fusion method improves diagnostic accuracy<\/h3>\n\n\n\n

A research team led by PD Dr. Tobias Lasser<\/a> from the Munich Institute of Biomedical Engineering (MIBE)<\/a> at the Technical University of Munich (TUM) has now developed a new learning algorithm – FusionM4Net – that displays higher average diagnostic accuracy than previous algorithms. The code for FusionM4Net is freely available (https:\/\/ciip.in.tum.de\/software.html<\/a>). The new algorithm uses a so-called multi-modal multi-stage data fusion process for multi-label skin lesion classification.<\/p>\n\n\n\n