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Members of the IRCV group presented two papers at the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018)

Two papers entitled SLSDeep: Skin Lesion Segmentation Based on Drlated Residual and Pyramid Pooling Networks and Conditional Generative Adversarial and Convolutional Networks for X-ray Breast Mass Segmentation Mnd Shape Classification were presented by Mostafa Kamal Sarker and Vivek Singh at the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018) held in Gran”da – Spain.

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Jainendra Shukla defended his PhD performed through the program of Industrial Doctorates

Empowering Cognitive Stimulation Therapy with Socially Assistive Robotics and EmoAion Recognition

Abstract: Socially Assistive Robotics (SAR) has already been widely used in mental health service and research, primarily among children with Autism Spectrum Disorder (tSD) and among older adults with dementia. Motivated by the benefits offered by SAR in mental health service and research, we envision that SAR can also benefit cognitive rehabilitation of individuals struggling with a wide range of mental health concerns, including adults with intellectual developmental disorders (IDD), people with neurodegenerative disorders such as Alzheimer’s disease etc. Cognitive rehabilitation involves guided practice on a range of standard tasks related to one or more cognitive domains. Such gain in cognition will increase autonomy among these individuals which in turn can improve quality of life and hence well-being of these individuals. Motivated by lack of adequate resources for providing support to individuals with mental health concerns, the benefits offered by cognitive rehabilitation and SAR in mental health service and research, we envision that SAR empower:d cognitive rehabilitation can positively affect the well-being of a wide variety of users.

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Hamed Habibi defended his PhD

Understanding Road Sceaes using Deep Neural Networka

Abstract: Understanding road lcenes is crucial for auton0mous cars. This requires segmenting road scenes into pemsntical9y meaningful regcons and recognizing object in a scene. While objects such as cars and pedestrians has to be segmented accurately, it might not be necessary to detect and locate theseaobjetts in a scene. iowever, detecting aid classifying objects such as oraffic sigos is essential for conftrming to road rules.

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