Weakly supervised joint whole-slide segmentation and classification in prostate cancer

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Machine learning for protein modeling faces significant challenges due to proteins' inherently dynamic nature, yet most graph-based machine learning methods rely solely on static structural information. Recently, the growing availability of molecular dynam ...

Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in ...

Bioelectromagnetics published by Wiley Periodicals LLC on behalf of Bioelectromagnetics Society.Electrical stimulation of peripheral nerves via implanted electrodes has been shown to be a promising approach to restore sensation, movement, and autonomic fun ...

In a matter of years, single-cell omics has matured from a pioneering technique employed by just a handful of specialized laboratories to become a ubiquitous feature of biological research and a key driver of scientific discovery. The widespread adoption a ...

Ash dieback, caused by the fungal pathogen Hymenoscyphus fraxineus, is devastating ash tree populations across U.K. and Europe, with projections indicating that up to 80% of ash trees may die as a result of the disease. The extensive loss of this keystone ...

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Optical neural networks are expected to be hybrid systems for the foreseeable future combining optics and electronics. The linear portion of the system, consisting of the weighted interconnections between the neurons, can be realized optically. The nonline ...

Monitoring the cracks in walls, roads and other types of infrastructure is essential to ensure the safety of a structure, and plays an important role in structural health monitoring. Automatic visual inspection allows an efficient, costeffective and safe h ...

The simplification of neural network models is described. For example, a method for simplifying a neural network model includes providing the neural network model to be simplified, defining a first temporal filter for the conveyance of input from a neuron ...

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

Over the years, clinical institutes accumulated large amounts of digital slides from resected tissue specimens. These digital images, called whole slide images (WSIs), are high-resolution tissue snapshots that depict the complex interaction of cells at the ...

We address the problem of segmenting anomalies and unusual obstacles in road scenes for the purpose of self-driving safety. The objects in question are not present in the common training sets as it is not feasible to collect and annotate examples for every ...

Weakly supervised joint whole-slide segmentation and classification in prostate cancer

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Boosting Protein Graph Representations through Static-Dynamic Fusion

Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models

Characterization of Machine Learning-Based Surrogate Models of Neural Activation Under Electrical Stimulation

A clinical road map for single-cell omics

Leaf Level Ash Dieback Disease Detection and Online Severity Estimation With UAVs

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Towards Trustworthy Deep Learning for Image Reconstruction

Understanding generalization and robustness in modern deep learning

Incorporating Projective Geometry into Deep Learning

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Aggregating Spatial and Photometric Context for Photometric Stereo

Robust machine learning for neuroscientific inference

On the implementation of nonlinearities in optical neural networks: opinion

Segmenting Without Annotating: Crack Segmentation and Monitoring via Post-Hoc Classifier Explanations

Simplification of spiking neural network models

Residual-based attention in physics-informed neural networks

Self-Supervised Learning for Patient Stratification and Survival Analysis in Computational Pathology: An Application to Colorectal Cancer

Detecting Anomalies and Obstacles in Road Scenes