Hi, I am Amelia, a third year Ph.D. candidate at the BCN MedTech group, under the supervision of Gemma Piella and Diana Mateus. My research interests are in the broad areas of medical imaging and deep learning.
I work on the design of data schedulers, i.e. a deep learning algorithm which determines the order and pace of instances presented to the optimizer, aiming to improve classification accuracy.
Specifically, I focus on curriculum-based strategies to develop data schedulers able to deal with label noise, limited amounts of data and class-imbalance, problems that are fairly common in medical datasets.
- August 2020: I will be attending ECCV 2020. I have attended the Deep Learning & Reinforcement Learning Summer School (DLRLSS 2020).
- June 2020: "Precise Proximal Femur Fracture Classification for Interactive Training and Surgical Planning" has been accepted for oral presentation at IPCAI 2020 and journal publication in IJCARS.
- June 2020: "Hierarchical Deep Curriculum Learning for the Classification of Proximal Femur Fractures" has been accepted for oral presentation at CARS 2020.
- Dec. 2019: Poster presentation at the Deep Learning Barcelona Symposium (DLBCN 2019).
- Oct. 2019: Poster presentation of "Medical-based Deep Curriculum Learning for Improved Fracture Classification" at MICAAI 2019 in Shenzhen, China.
- Sept. 2018: Oral and poster presentation of "Capsule Networks against Medical Imaging Data Challenges" at LABELS Workshop - MICCAI 2018 in Granada, Spain).
- July 2018: I have attended PAISS Summer School 2018 in Grenoble, France.