This is an interdisciplinary project that aims to classify the degree to which of bladder lesions invade the bladder walls, which is critical to assessing the patient's treatment.
The project is inpired by VI-RADS (a standardization of MRI acquisition and interpretation that yields a score in range [1, 5]) that has been shown to be effective in medical studies.
In this project, deep learning methods will be explored as a classifier of the scale of bladder lesions. It includes elicitation sessions with radiologists.
FL is a new methodology providing privacy in learning by sharing only model parameters rather than the data directly with the server. In this project, we aim to apply well-known FL techniques to wearable data collected from university students and workers and compare them with traditional ML techniques. The dataset includes activity, sleep, and stress-related information.
In this project, you are going to implement and build upon our recently proposed symbol learning method, namely DeepSym [1], and make new experiments on new domains to discover the benefits and shortcomings of the method.
Humans can interact with objects differently by using only one hand. The grippers on the robot can be used for different actions too. Via using the contact points on the objects and detecting the movements at these points. The actions should be recognized.
In robotic, simulators are a requirement for improvement of the algorithms without causing any harm, so existing robots should be transferred to asimilasyon environment. We need to use KIT Gripper with ROS + Gazebo environment with position controller. Also, existing robot should have similar inputs with the simulations. So, we need a ROS Wrapper which takes data from real robot and converts it to required ROS messages. After environment development, the system will be tested via Human - Robot cooperative game.
Wearable devices can capture multimodal data corresponding to a person’s activity, stress, sleep information to measure and improve health and well-being. Besides device measurement, there are also survey data as another information gathering methodology.
Wearable devices can capture multimodal data corresponding to a person’s activity, stress, sleep information to measure and improve health and well-being. Besides device measurement, there are also survey data as another information gathering methodology.
