This paper investigates discrepancies in how neural networks learn from different imaging
domains, which are commonly overlooked when adopting computer vision techniques from …

We explore the intersection of neural dynamics and the effects of psychedelics in light of
distinct timescales in a framework integrating concepts from dynamics, complexity, and …

Background and objective: This paper introduces an encoder–decoder-based attentional
decoder network to recognize small-size lesions in chest X-ray images. In the encoder-only …

The image acquisition parameters (IAPs) used to create MRI scans are central to defining
the appearance of the images. Deep learning models trained on data acquired using certain …

The last decade of computer vision has been dominated by Deep Learning architectures,
thanks to their unparalleled success. Their performance, however, often comes at the cost of …

We introduce the Binless Multidimensional Thermodynamic Integration (BMTI) method for
nonparametric, robust, and data-efficient density estimation. BMTI estimates the logarithm of …

Automatic characterization of malignant disease is an important clinical need to facilitate
early detection and treatment of cancer. A deep semi-supervised transfer learning approach …

The demand for large quantities of high-quality annotated images poses a significant
bottleneck for developing effective deep learning-based classifiers in the biomedical …