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Capsule Networks against Medical Imaging Data Challenges

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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. Read more

Medical-based Deep Curriculum Learning for Improved Fracture Classification

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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. Read more

Precise proximal femur fracture classification for interactive training and surgical planning

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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. Read more

Learning Representations for Medical Image Diagnosis: Impact of Curriculum Training and Architectural Design

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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. Read more