Talk, Helmholtz AI, Munich, Germany
This talk presents research work on the implementation of curriculum learning as a data scheduler for medical image diagnosis. In particular, for the application of proximal femur fractures classification, and breast cancer classification in a federated setting.
Talk, DTIC, Universitat Pompeu Fabra, Barcelona, Spain
This talk summarizes the main outcomes of my PhD thesis. We investigated two key aspects to learn feature representations leveraging Convolutional Neural Networks from medical images for Computer-Aided Diagnosis tasks. In the first part, we explored the role of architectural design in dealing with spatial information. In the second part, we designed curriculum training strategies to control the order, pace, and number of images presented to the optimizer.
Talk, Computer Assisted Radiology and Surgery – CARS, Munich (virtual), Germany
This talk presents a deep curriculum learning strategy that leverages hierarchical information for the fine-grained classification of proximal femur fractures. Our results indicate that using broader labels helps to reduce errors and improve the overall detailed classification.
Talk, International Conference on Information Processing in Computer-Assisted Interventions – IPCAI, Munich (virtual), Germany
This talk presents the feasibility of a fully automatic computer-aided diagnosis tool, based on deep learning, that localizes and classifies proximal femur fractures on X-ray images according to the AO classification. The proposed framework aims to improve patient treatment planning and provide support for the training of trauma surgeon residents. The presentation is available here.
Talk, International Conference on Medical Image Computing and Computer Assisted Interventions – MICCAI, Shenzhen, China
This talk presents strategies derived from knowledge such as medical decision trees and inconsistencies in the annotations of multiple experts, which allow us to assign a degree of difficulty to each training sample. We demonstrate that if we start learning “easy” examples and move towards “hard”, the model can reach a better performance, even with fewer data.
Talk, Large-Scale Annotation of Biomedical Data and Expert Label Synthesis – LABELS Workshop at MICCAI, Granada, Spain
This talk compares the behavior of capsule networks against convolutional neural networks under typical datasets constraints of medical image analysis, namely, small amounts of annotated data and class-imbalance. We evaluate our experiments on MNIST, Fashion-MNIST and medical (histological and retina images) publicly available datasets. Our results suggest that capsule networks can be trained with less amount of data for the same or better performance and are more robust to an imbalanced class distribution, which makes our approach very promising for the medical imaging community.