Neural networks, and more broadly, machine learning techniques, have been recently
exploited to accelerate topology optimization through data-driven training and image …

The rapid development of deep learning has opened a new door to the exploration of
topology optimization methods. The combination of deep learning and topology optimization …

Today, deep convolutional neural networks (CNNs) have demonstrated state of the art
performance for supervised medical image segmentation, across various imaging modalities …

OCT fluid segmentation is a crucial task for diagnosis and therapy in ophthalmology. The
current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks …

We propose a novel weakly supervised learning segmentation based on several global
constraints derived from box annotations. Particularly, we leverage a classical tightness prior …

Deep-learning-based registration methods emerged as a fast alternative to conventional
registration methods. However, these methods often still cannot achieve the same …

Background and objective: During the initial stages, skin lesions may not have sufficient
intensity difference or contrast from the background region on dermatological macro-images …

Minimising upper bounds on the population risk or the generalisation gap has been widely
used in structural risk minimisation (SRM)--this is in particular at the core of PAC-Bayesian …

The focus of this paper is on multi-material topology optimization (MMTO), where the
objective is to not only compute the optimal topology, but also the distribution of two or more …

Weakly-supervised learning (WSL) has recently triggered substantial interest as it mitigates
the lack of pixel-wise annotations. Given global image labels, WSL methods yield pixel-level …