TECVICO

This challenge has been held twice so far, and registration for the third time will begin soon. On this occasion, TECVICO invites all scientists and researchers interested in the field to join TECVICO team and participate in this challenge. Participation in this challenge, which is one of the most prestigious research calls in the world in the field of medical image processing, will be possible only as a team. TECVICO has a history of participating in the previous two editions of this challenge, winning prestigious awards. Tecvico competed for the second time in HECKTOR challenge as the Tecvico_Corp_Family team, which resulted in being awarded the sixth place in the competition.

Feature selection and machine learning methods for optimal identification and prediction of subtypes in Parkinson's disease July 2021: TECVICO, the paper named Feature selection and machine learning methods for optimal identification and prediction of subtypes in Parkinson's disease published in the Journal of Computer Methods and Programs in Biomedicine. For more information, you can click on the link below. Authors: Mohammad R.Salmanpour, Mojtaba Shamsaei, ArmanRahmim

AI can be found in every aspect of our lives, from email spam filtering and Autonomous Vehicles to security and medical diagnostics. Particularly deep learning has been one of the key innovations that have greatly impacted exponential growth in various scientific fields. Although deep learning has proved both in theory and in action to have limitless capabilities, making a deep neural network for new application areas, remains challenging due to its complex nature and also lack of any clear paradigm to create a new customized DNN. So, designing an architecture for an intended purpose requires proficiency and often a lot of luck! Inspired by the Gradient Boosting algorithm, creators of this model have introduced a novel approach that builds neural networks from the ground up layer by layer. Gradient Boosting algorithm incrementally builds up sophisticated models out of simpler components or so-called “Weak Learners”, that can successfully be applied to the most complex learning tasks. ...