Spotiflow: accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression

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Background and Objective: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multiple app ...

The increasing accumulation of medical data brings the hope of data-driven medical decision-making, but data's increasing complexity—as text or images in electronic health records—calls for complex models, such as machine learning. Here, we review how mach ...

Visual anomaly detection (AD) presents significant challenges due to the scarcity of anomalous data samples. While numerous works have been proposed to synthesize anomalous samples, these synthetic anomalies often lack authenticity or require extensive tra ...

Since the 1960s, the prevalence of pollen allergy has increased worldwide in developed countries and this trend is continuing, exacerbated by climate change. In the 20th century, operational pollen monitoring networks have been set-up to respond to the nee ...

Cross-view localization, the task of estimating a camera's 3-degrees-of-freedom (3-DoF) pose by aligning ground-level images with satellite images, is crucial for large-scale outdoor applications like autonomous navigation and augmented reality. Existing m ...

This thesis is concerned with improving the interpretability of network summaries in both a theoretical and an applied framework, with an application to clinically relevant problems in neuroscience. The theoretical part of this thesis is concerned with gra ...

Holography enables quantitative phase sensing through light wave interference, allowing interesting imaging modalities applied in computational microscopy where phase shift is used as a way to generate image contrast. Recovering both amplitude and phase di ...

Quantitative analysis of microstructural features on the nanoscale, including precipitates, local chemical orderings (LCOs) or structural defects (e.g. stacking faults) plays a pivotal role in understanding the mechanical and physical responses of engineer ...

Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, re ...

3D Fluorescence Molecular Tomography (FMT) is an important pre-clinical tool for quantifying and characterizing molecular markers for different diseases. Herein, we demonstrate, for the first time, 3D K-Space FMT reconstructions using a large time-gated SP ...

Challenges inherent to high-resolution and high signal-to-noise data as well as model degeneracies can cause systematic biases in analyses of strong lens systems. In the past decade, the number of lens modeling methods has significantly increased, from pur ...

Recent advances in deep learning and Vision-Language Models (VLM) have enabled efficient transfer to downstream tasks even when limited labelled training data is available, as well as for text to be directly compared to image content. These properties of V ...

Electronic computers have evolved drastically over the past years with an ever-growing demand for improved performance. However, the transfer of information from memory and high energy consumption have emerged as issues that require solutions. Optical tech ...

Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks as the curse of dimensionality (CoD) cannot be evaded when trying to approximate even a single ReLU neuron (Bach, 2017). In thi ...

With the significant increase in photovoltaic (PV) electricity generation, more attention has been given to PV power forecasting. Indeed, accurate forecasting allows power grid operators to better schedule and dispatch their assets, such as energy storage ...

Developing computational pathology models is essential for reducing manual tissue typing from whole slide images, transferring knowledge from the source domain to an unlabeled, shifted target domain, and identifying unseen categories. We propose a practica ...

Deep convolutional neural networks (DCNNs) have significantly advanced the field of computer vision and are often considered as plausible models for the human visual system. However, they still have several vulnerabilities in terms of adversarial attacks, ...

Reasoning about distance is indispensable for establishing or avoiding contact in manipulation tasks. To this end, we present an online approach for learning implicit representations of signed distance using piecewise polynomial basis functions. Starting f ...

Detecting learners in need of support is a complex process for both teachers and machines. Most prior work has devised visualization tools that allow teachers to do so by analyzing educational indicators. Other recent efforts have been devoted to models th ...

Sensor technologies empower Industry 4.0 by enabling integration of in-field and real-time raw data into digital twins (DTs). However, sensors might be unreliable due to inherent issues and/or environmental conditions. This article aims at detecting anomal ...

Spotiflow: accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression

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FADE: Forecasting for anomaly detection on ECG

From Prediction to Prescription: Machine Learning and Causal Inference for the Heterogeneous Treatment Effect

Anomaly Anything: Promptable Unseen Visual Anomaly Generation

Automatic identification of atmospheric bioaerosol using real-time imaging and fluorescence

GeoDistill: Geometry-Guided Self-Distillation for Weakly Supervised Cross-View Localization

Nonparametric network summaries

Cross-Net: Joint In-Line Holographic Image Reconstruction and Refocusing

3D deep learning for enhanced atom probe tomography analysis of nanoscale microstructures

SuperAnimal pretrained pose estimation models for behavioral analysis

3D fluorescence molecular tomography utilizing a novel SPAD camera

Exploiting the diversity of modeling methods to probe systematic biases in strong lensing analyses

Prompt-guided and multimodal landscape scenicness assessments with vision-language models

Programming the scalable optical learning operator with spatial-spectral optimization

Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks

Intraday solar irradiance forecasting using public cameras

Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology

Towards Robust Vision Models

Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials

How Close are Predictive Models to Teachers in Detecting Learners at Risk?

A Machine-Learning Architecture for Sensor Fault Detection, Isolation, and Accommodation in Digital Twins