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Hi, I am Amelia. I recently completed my Ph.D. at Pompeu Fabra University. I was a member of the BCN MedTech group. My research has been supervised by Gemma Piella and Diana Mateus. My research aims at learning representations for medical image diagnosis facing common medical imaging data challenges, namely limited data, class-imbalance, noisy annotations and data privacy. I hold a degree in Telecommunications Engineering from the University of Granada and a Master of Science in Biomedical Computing from the Technical University of Munich.

In my thesis, we investigated two key aspects to learn feature representations leveraging Convolutional Neural Networks from medical images for Computer-Aided Diagnosis tasks. First, we explored the role of architectural design in dealing with spatial information. Second, we designed curriculum training strategies to control the order, pace, and number of images presented to the optimizer.

My dissertation is entitled "Learning Representations for Medical Image Diagnosis: Impact of Curriculum Training and Architectural Design". You can both watch it or download it.

• October 14th 2021: I have successfully defended my thesis "Learning Representations for Medical Image Diagnosis: Impact of Curriculum Training and Architectural Design". Thrilled to have been awarded with the mention "Cum Laude".
• December 2020: "Curriculum Learning to Deal with Noisy Labels" accepted for oral presentation at iTWIST'20.
• August 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.