In-Silico Trained AI for Enhanced T<inf>2</inf>Spectrum Imaging and Myelin Water Fraction Mapping in Preclinical 7T MRI

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The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approaches generally issue predictions using data from a single modality. This stan ...

Deep neural networks often fail to generalize when deployed in dynamic and unpredictable real-world environments, where data distributions at test time can differ significantly from those encountered during training. Since simulating all possible distribut ...

This paper studies the probability of error associated with the social machine learning framework, which involves an independent training phase followed by a cooperative decision-making phase over a graph. This framework addresses the problem of classifyin ...

Designing AI-based applications personalized to each user's behavior presents significant challenges due to the cold start problem and the impracticality of extensive individual data labeling. These challenges are further compounded when deploying such app ...

Lensless cameras relax the design constraints of traditional cameras by shifting image formation from analog optics to digital post-processing. While new camera designs and applications can be enabled, lensless imaging is very sensitive to unwanted interfe ...

Introduction Quantifying the myelin sheath radius of myelinated axons in vivo is important for understanding, diagnosing, and monitoring various neurological disorders. Despite advancements in diffusion MRI (dMRI) microstructure techniques, there are curre ...

Purpose: Phosphorus MRS (31P MRS) enables noninvasive assessment of energy metabolism, yet its application is hindered by sensitivity limitations. To overcome this, often high magnetic fields are used, leading to challenges such as spatial (Formula present ...

In this paper, we address the challenge of few-shot classification in histopathology whole slide images (WSIs) by utilizing foundational vision-language models (VLMs) and slide-level prompt learning. Given the gigapixel scale of WSIs, conventional multiple ...

DARTS-NETGAB is a unified framework for real-time simulation and automated design of gas-bearing supported turbocompressors, facilitating efficient transition from optimization to manufacturable designs. The framework integrates ensemble artificial neural ...

As medical datasets rapidly expand, creating detailed annotations of different body structures becomes increasingly expensive and time-consuming. We consider that requesting radiologists to create detailed annotations is unnecessarily burdensome and that p ...

Animal motor control arises from the intricate interplay between neural circuits, biomechanics, and sensory feedback. Deciphering how these components interact is essential for uncovering the fundamental principles of movement generation. This dissertation ...

The visual system can process diverse stimuli and make the decision to execute appropriate behaviors, but it remains unclear where and how this transformation takes place. Innate visually evoked behaviors such as hunting, freezing, and escape are thought t ...

The growing availability of distributed data has led to the increased use of machine learning (ML) algorithms in distributed topologies, where multiple nodes collaborate to train models under the coordination of a central server. However, distributed learn ...

Motivation is a multifaceted phenomenon that we explore within the framework of decision-making. Through this cognitive process, actions are directed towards specific goals by performing a trade-off between the cost and benefit of an action. The dorsomedia ...

Multiple drug concentrations concurrent detection and quantification based on electrochemical sensors are of great importance for therapeutic drug monitoring (TDM) and the development of personalized therapy. Cyclic voltammogram (CV) results obtained by el ...

The contextual stochastic block model (CSBM) was proposed for unsupervised community detection on attributed graphs where both the graph and the high-dimensional node information correlate with node labels. In the context of machine learning on graphs, the ...

Deep learning has achieved remarkable success in diverse fields in recent years. However, this growth presents significant challenges, particularly in terms of energy consumption during both training and inference phases. While there have been efforts to i ...

Copyright © 2024 Meystre, Jacquemier, Burri, Zsolnai, Frank, Vieira, Shi, Perin, Keller and Markram.Nissl histology underpins our understanding of brain anatomy and architecture. Despite its importance, no high-resolution datasets are currently available i ...

Previous methods for predicting room acoustic parameters and speech quality metrics have focused on the single-channel case, where room acoustics and Mean Opinion Score (MOS) are predicted for a single recording device. However, quality-based device select ...

Novice students in law courses or students who encounter legal education face the challenge of acquiring specialized and highly concept-oriented knowledge. Structured and persuasive writing combined with the necessary domain knowledge is challenging for ma ...

In-Silico Trained AI for Enhanced T<inf>2</inf>Spectrum Imaging and Myelin Water Fraction Mapping in Preclinical 7T MRI

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Automated Ensemble Multimodal Machine Learning for Healthcare

Adaptive Test-Time Learning Under Domain Shift: From Augmentation to Prolonged Adaptation

Non-asymptotic performance of social machine learning under limited data

Solving the Cold-Start Problem for the Edge: Clustering and Adaptive Deep Learning for Emotion Detection

Let There Be Light: Robust Lensless Imaging Under External Illumination With Deep Learning

A diffusion MRI model for random walks confined on cylindrical surfaces: towards non-invasive quantification of myelin sheath radius

Fast 3D 31P B1+ mapping with a weighted stack of spiral trajectory at 7 T

Slide-Level Prompt Learning with Vision Language Models for Few-Shot Multiple Instance Learning in Histopathology

DARTS-NETGAB: Design Automation and Real-Time Simulation Using Neural Network Ensembles for Turbocompressors on Gas-Bearings

Label Critic: Design Data Before Models

An integrative computational modeling approach for Drosophila motor control

The visuomotor transformations underlying target-directed behavior

Towards Practical Homomorphic Aggregation in Byzantine-Resilient Distributed Learning

Neurobiological and metabolic basis of motivation and effort-based decision-making - A human 1H-MRS, fMRI and machine learning study

Automatic simulation of electrochemical sensors by machine learning for drugs quantification

Optimal Inference in Contextual Stochastic Block Models

Backpropagation-free Training of Analog AI Accelerators

Cell density quantification of high resolution Nissl images of the juvenile rat brain

Multi-Channel Mosra: Mean Opinion Score and Room Acoustics Estimation Using Simulated Data and A Teacher Model

LegalWriter: An Intelligent Writing Support System for Structured and Persuasive Legal Case Writing for Novice Law Students