Publications associées à 3D fluorescence molecular tomography utilizing a novel SPAD camera

Publications associées (7)

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

Emine Berna Durmus, Suliana Manley, Joachim Lingner, Gioele La Manno, Martin Weigert, Irina Khven, Can Aztekin, Eftychia Kyriacou, Albert Dominguez Mantes, Anjalie Olivia Schläppi, Georgios Tsissios, Evangelia Skoufa

Identification of spot-like structures in large, noisy microscopy images is a crucial step for many life-science applications. Imaging-based spatial transcriptomics (iST), in particular, relies on the precise detection of millions of transcripts in low sig ...

Nature Publishing Group2025

FADE: Forecasting for anomaly detection on ECG

David Atienza Alonso, Denisa-Andreea Constantinescu

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 ...

Elsevier BV2025

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

Ye Wei

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 ...

2024

Programming the scalable optical learning operator with spatial-spectral optimization

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ilker Oguz, Mustafa Yildirim, Carlo Gigli, Jih-Liang Hsieh, Yi Zhou Ju

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 ...

2024

Towards Robust Vision Models

Harshitha Machiraju

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, ...

EPFL2024

Unsupervised Controllable Generation with Self-Training

Grigorios Chrysos

Recent generative adversarial networks (GANs) are able to generate impressive photo-realistic images. However, controllable generation with GANs remains an open research problem. Achieving controllable generation requires semantically interpretable and dis ...

IEEE2021

Accelerating Inference on Binary Neural Networks with Digital RRAM Processing

David Atienza Alonso, Marina Zapater Sancho, Yasir Mahmood Qureshi

The need for efficient Convolutional Neural Network (CNNs) targeting embedded systems led to the popularization of Binary Neural Networks (BNNs), which significantly reduce execution time and memory requirements by representing the operands using only one ...

Springer Nature2020