Computer Vision News - November 2021

Amelia Jiménez-Sánchez recently completed her Ph.D. at Pompeu Fabra University . Her research aims at learning representations for medical image diagnosis facing common medical imaging data challenges , namely limiteddata, class-imbalance, noisy annotations and data privacy. She holds a degree in Telecommunications Engineering from the University of Granada and a Master of Science in Biomedical Computing from the Technical University of Munich. She’s been awarded the mention “Cum Laude” for her Ph.D. Congrats cum laude, Doctor Amelia! In this thesis, we investigate two key aspects to learn feature representations leveraging Convolutional Neural Networks ( CNNs ) frommedical images for Computer- Aided Diagnosis (CAD) tasks. First, we explore the role of architectural design in dealing with spatial information. Second, we design curriculum training strategies to control the order, pace, and number of images presented to the optimizer. Capsule networks CNN’s requirement for big amounts of data is commonly justified by a large number of network parameters to train under a non-convex optimization scheme. We argue, however, that part of these data requirements is there to cope with their poor modeling of spatial invariance . Capsule networks were introduced as an alternative deep learning architecture and training approach to model the spatial/viewpoint variability of an object in the image. We experimentally demonstrate that the equivariance properties of capsule networks reduce the strong data requirements , namely limited amount of data and class-imbalance, and are therefore very promising for medical image analysis. Ordering and pacing In a typical educational system, learning relies on a curriculum that introduces new concepts building upon previously acquired ones. The rationale behind, is that humans and animals learn better when information is presented in a meaningful way rather than randomly. We design Curriculum Learning (CL) strategies for the fine-grained classification of proximal femur fractures according to the AO standard. Our novel framework reunites three strategies consisting of individually weighting training samples, reordering the training set, or sampling subsets of data. We define the scoring function from domain-specific prior knowledge or by directly measuring the uncertainty in the predictions. 72 Congrats (cum laude), Doctor!