Nissl_4, Raw images for Machine learning for histological annotation and quantification of cortical layers

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Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to catheteri ...

This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. The algorithm retains high ...

Estimating ecosystem-atmosphere fluxes such as evapotranspiration (ET) in a robust manner and at a global scale remains a challenge. Methods based on machine learning (ML) have shown promising results in achieving such upscaling, providing a complementary ...

Brain organoids enable the mechanistic study of human brain development and provide opportunities to explore self-organization in unconstrained developmental systems1, 2–3. Here we establish long-term, live light-sheet microscopy on unguided brain organoid ...

This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer ...

To retrieve Surface Solar Radiation (SSR) from satellite images, a baseline reflectance of the observed ground in clear-sky conditions, also called background reflectance, is necessary to distinguish atmospheric absorption and scattering effects from surfa ...

Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at ...

The study of brain disorders has long been central to both neurology and psychiatry, with these two disciplines evolving as distinct yet deeply interconnected fields. In fact, these specialties often share overlapping symptoms, neural pathways, and treatme ...

The digitization of 3D deformable objects remains a significant challenge in computer graphics and vision, particularly in the accurate modeling of garments. Garments exhibit complex shape variability, non-rigid deformations, and frequent self-occlusion, m ...

A novel fast 3D-31P-B+1 mapping method called the frequency-selective Double-AngleMethod (fDAM) has been developed. This method integrates a 3D weighted stack of spiral gradient echo acquisitions with a frequency-selective pulse to efficiently map B+1 fiel ...

Over the past decade, research has shown that the primary motor cortex (M1), the brain's main output for movement, also responds to rewards. These reward signals may shape motor output in its final stages, influencing movement invigoration and motor learni ...

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (US ...

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

BackgroundBronchiolitis Obliterans Syndrome (BOS), a fibrotic airway disease that may develop after lung transplantation, conventionally relies on pulmonary function tests (PFTs) for diagnosis due to limitations of CT imaging. Deep neural networks (DNNs) h ...

Correlative light and electron microscopy (CLEM) combines light microscopy, for identifying a target via genetic labels, dyes, antibodies and morphological features, with electron microscopy, for analyzing high-resolution subcellular ultrastructures. Here, ...

Over the last two decades, Diffusion-Weighted Magnetic Resonance Imaging (DW-MRI) has been developed as a powerful MR imaging modality that allows estimating non-invasively the microscopic structure of biological tissues by exploiting the natural motion of ...

Jointly estimating hand and object shape facilitates the grasping task in human-to-robot handovers. Relying on handcrafted prior knowledge about the geometric structure of the object fails when generalising to unseen objects, and depth sensors fail to dete ...

Given a ground-level query image and a geo-referenced aerial image that covers the query’s local surroundings, fine-grained cross-view localization aims to estimate the location of the ground camera inside the aerial image. Recent works have focused on dev ...

A robust anomaly detection mechanism should possess the capability to effectively remediate anomalies, restoring them to a healthy state, while preserving essential healthy information. Despite the efficacy of existing generative models in learning the und ...

Automatic speech recognition (ASR) systems are well known to perform poorly on dysarthric speech. Previous works have addressed this by speaking rate modification to reduce the mismatch with typical speech. Unfortunately, these approaches rely on transcrib ...

Nissl_4, Raw images for Machine learning for histological annotation and quantification of cortical layers

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Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

Enhancing skin lesion classification: a CNN approach with human baseline comparison

On the added value of sequential deep learning for the upscaling of evapotranspiration

Morphodynamics of human early brain organoid development

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

Retrieval of Surface Solar Radiation through Implicit Albedo Recovery from Temporal Context

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning

The Interdisciplinary Synergy Between Neurology and Psychiatry: Advancing Brain Health

Neural Garment Modeling

Fast Quantification of Creatine Kinase Activity by Phosphorus Magnetic Resonance Fingerprinting

Reward signals in the motor cortex: from biology to neurotechnology

Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Harnessing deep learning to detect bronchiolitis obliterans syndrome from chest CT

Correlative light and electron microscopy for human brain and other biological models

Simulation-based Microstructure Imaging: Towards Realistic White Matter Modeling using Monte Carlo Methods in DW-MRI

Stereo Hand-Object Reconstruction for Human-to-Robot Handover

Adapting Fine-Grained Cross-View Localization to Areas Without Fine Ground Truth

Self-Supervised Anomaly Segmentation via Diffusion Models with Dynamic Transformer UNet

Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR