Title: Multi-Task Network for Computed Tomography Segmentation Through Fractal Dimension Estimation
Aziza Saber Jabdaragh, Ms. Student in Computer Engineering
Advisor: Prof. Dr. Çiğdem Gündüz Demir
Co-Advisor: Prof.Dr. Selim Aksoy
Member: Asst. Prof. Dr. Shervin Rahimzadeh Arashloo
Assoc. Prof. Dr. Tunca Doğan (Hacettepe Üniversity)
The Seminar will be on Wednesday, January 18, 2023, 16:30 pm İstanbul
This is an online seminar. To request the event link, please send a message to department.
Abstract: Multi-task learning proved to be an effective strategy to increase the performance of a dense prediction network on a segmentation task by defining auxiliary tasks to reflect different aspects of the problem and concurrently learning them with the main task of segmentation. Up to now, previous studies defined their auxiliary tasks in the Euclidean space. However, for some segmentation tasks, the complexity and high variation in the texture of a region of interest may not follow the smoothness constraint in the Euclidean geometry. This thesis addresses this issue by introducing a new multi-task network, MTFD-Net, which utilizes the fractal geometry to quantify texture complexity through self-similar patterns in an image. To this end, we propose to transform an image into a map of fractal dimensions and define its learning as an auxiliary task, which will provide auxiliary supervision to the main segmentation task, towards betterment of left atrium segmentation in computed tomography images. To the best of our knowledge, this is the first proposal of a dense prediction network that employs the fractal geometry to define an auxiliary task and learns it in parallel to the segmentation task in a multi-task learning framework. Our experiments revealed that the proposed MTFD-Net model led to more accurate left atrium segmentations compared to its counterparts.