Hierarchical graph representations in digital pathology

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Computational Imaging (CI) is the field concerned with reconstructing images of some quantity of interest from a finite number of measurements. It is pervasive in science and engineering problems by enabling the reconstruction of images from a variety of m ...

In crowding, perception of a target deteriorates in the presence of nearby elements. As the entire stimulus configuration across large parts of the visual field influences crowding and not just nearby elements, low-level explanations, such as local pooling ...

Uncovering the neural architectures that enable robust and flexible motor control is a fundamental challenge in neuroscience. Understanding the neural circuitry organisation at a cellular level is crucial for advancing our knowledge and developing interven ...

Lung cancer is the leading cause of cancer-related deaths worldwide and the most common lung cancer subtype is lung adenocarcinoma (LUAD). Frequently mutated genes involve activating mutations in KRAS and loss of function mutations in TP53. LUADs primarily ...

This workshop, inspired by the Archimedean Oath, seeks to engage participants in critically reflecting on the responsibilities of engineers in society. Following the update of the content of the Oath to address contemporary challenges, the workshop aims at ...

Fluorescence confocal laser-scanning microscopy (LSM) is one of the most popular tools for life science research. This popularity is expected to grow thanks to single-photon array detectors tailored for LSM. These detectors offer unique single-photon spati ...

Implantable neural interfaces with the central and peripheral nervous systems are currently used to restore sensory, motor, and cognitive functions in disabled people with very promising results. They have also been used to modulate autonomic activities to ...

The application of microfluidics in the field of surface-based assays and more specifically, the spatial molecular profiling of tumor tissues has gained a lot of interest, especially with the increased interest in personalized medicine and targeted therapy ...

Purpose: To introduce the dipolectric antenna: a novel RF coil design for high-field MRI using a combination of a dipole antenna with a loop-coupled dielectric resonator antenna. ...

There is a lack of knowledge about the accuracy of the Conventional Gait Model (CGM), compared to the true bone motion. Accuracy is hindered by both marker misplacement and soft-tissue artefact (STA). The effect of the lateral knee marker (KNE) misplacemen ...

We present the design and validation of an isotropic field probe with geometry and frequency response shaped to correctly weight the aggregate contribution of the various frequency components of a broadband magnetic field in the band 100 kHz - 400 MHz acco ...

The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of ...

Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical success, we still do not understand how INRs represent signals. We propose a ...

The automatic recognition of food on images has numerous interesting applications, including nutritional tracking in medical cohorts. The problem has received significant research attention, but an ongoing public benchmark on non-biased (i.e., not scraped ...

Deep Neural Networks (DNNs) have achieved great success in a wide range of applications, such as image recognition, object detection, and semantic segmentation. Even though the discriminative power of DNNs is nowadays unquestionable, serious concerns have ...

Cyber-physical systems (CPSs) integrate heterogeneous systems and process sensor data using digital services. As the complexity of CPS increases, it becomes more challenging to efficiently formalize the integrated multidomain views with flexible automated ...

Selective attention is a fundamental cognitive mechanism that allows our brain to preferentially process relevant sensory information, while filtering out distracting information. Attention is thought to flexibly gate the communication of irrelevant inform ...

Signal recovery under generative neural network priors has emerged as a promising direction in statistical inference and computational imaging. Theoretical analysis of reconstruction algorithms under generative priors is, however, challenging. For generati ...

Hierarchical graph representations in digital pathology

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Computational Imaging: a Multi-Facet Perspective

Does surface completion fail to support uncrowding?

Uncovering neural architectures enabling robust and flexible motor control in Drosophila

The role of LRRK2 in lung adenocarcinoma

Archimedean Oath: A reflection tool for responsible engineers

Compact and effective photon-resolved image scanning microscope

Organ Neuroprosthetics: Connecting Transplanted and Artificial Organs with the Nervous System

Development of next-generation microfluidic systems for enhanced, faster, and cost-effective immunoassays for tissue diagnostics - Ac electrothermal flow & Acoustofluidics

Dipolectric antenna for high-field MRI

On the accuracy of the Conventional gait Model: Distinction between marker misplacement and soft tissue artefact errors

Isotropic Magnetic Field Probe with ICNIRP 2020 Frequency Shaping in the Band up to 400 MHz

A new age in protein design empowered by deep learning

A Structured Dictionary Perspective on Implicit Neural Representations

The Food Recognition Benchmark: Using Deep Learning to Recognize Food in Images

Understanding Deep Neural Networks using Adversarial Attacks

Model-Based Systems Engineering Tool-Chain for Automated Parameter Value Selection

Selective attention involves a feature-specific sequential release from inhibitory gating

Multi-layer State Evolution Under Random Convolutional Design