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Anatomical foundation models for brain MRIs
Deep Learning (DL) in neuroimaging has become increasingly relevant for detecting neurological conditions and neurodegenerative …
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Capsule networks do not need to model everything
Capsule networks are biologically inspired neural networks that group neurons into vectors called capsules, each explicitly …
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TEP-ones: A simple yet effective approach for transferability estimation of pruned backbones
In deep learning, the conventional transfer learning paradigm involves fine-tuning a model pre-trained on a complex source task to …
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Unsupervised contrastive analysis for anomaly detection in brain MRIs via conditional diffusion models
Contrastive Analysis (CA) detects anomalies by contrasting patterns unique to a target group (e.g., unhealthy subjects) from those in a …
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AA-SGAN: Adversarially Augmented Social GAN with Synthetic Data
Accurately predicting pedestrian trajectories is crucial in applications such as autonomous driving or service robotics, to name a few. …
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Character recognition in Byzantine seals with deep neural networks
Seals are small coin-shaped artifacts, mostly made of lead, held with strings to seal letters. This work presents the first attempt …
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Detection of subclinical atherosclerosis by image-based deep learning on chest X-ray
Aims To develop a deep-learning-based system for recognition of subclinical atherosclerosis on a plain frontal chest X-ray. Methods and …
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Multi-target Stain Normalization for Histology Slides
Traditional staining normalization approaches, e.g. Macenko, typically rely on the choice of a single representative reference image, …
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Robust and efficient airplane cockpit video coding leveraging temporal redundancy
Airplane cockpit screens consist of virtual instruments where characters, numbers, and graphics are overlaid on a black or natural …
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Say My Name: a Model’s Bias Discovery Framework
Due to the broad applicability of deep learning to downstream tasks and end-to-end training capabilities in the last few years, …
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