Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: A multi-reader study

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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 ...

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 CRC-aided successive cancellation list (CA-SCL) decoding algorithm for polar codes has gained widespread adoption thanks to its outstanding performance. However, with the evolution of 6G technologies, the high complexity of CA-SCL decoding poses a chal ...

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 ...

Critical thinking is vital in every education system. Digital tools like Large Language Models (LLMs), exemplified by ChatGPT, are part of a new scheme of education; studying its influence on critical thinking (CT) is a rising research field with a need fo ...

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 ...

BACKGROUND: Multidisciplinary Heart Teams (HTs) play a central role in the management of valvular heart diseases. However, the comprehensive evaluation of patients' data can be hindered by logistical challenges, which in turn may affect the care they recei ...

Little is understood about how chatbots powered by Large Language Models (LLMs) impact teaching and learning, and how to effectively integrate them into educational practices. This study examined whether and how using ChatGPT in a graduate-level robotics c ...

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 ...

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 ...

Collaborative learning is an important tool to train multiple clients more effectively by enabling communication among clients. Identifying helpful clients, however, presents challenging and often introduces significant overhead. In this paper, we model cl ...

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 ...

In recent years, the field of Natural Language Processing has seen significant revolution by the introduction of Transformers, a stack of multiple layers of attention and non-linearity, capable of performing almost any task and the backbone for large found ...

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 ...

This study evaluates the efficiency of an explainable ensemble learning framework in precisely predicting the bond strength between steel sections with different surface treatments and various concrete types. Besides seven numerical features, two categoric ...

Computer-aided clinical decision support tools for radiology often suffer from poor generalizability in multi-centric frameworks due to data heterogeneity. In particular, magnetic resonance images depend on a large number of acquisition protocol parameters ...

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 ...

Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential decision making. In this paper, we examine the problem of infusing RL agents wit ...

Assessing workflow impact and clinical utility of AI-assisted brain aneurysm detection: A multi-reader study

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Anomaly Anything: Promptable Unseen Visual Anomaly Generation

FADE: Forecasting for anomaly detection on ECG

GenPolar: Generative AI-Aided Complexity Reduction for Polar SCL Decoding

Nonparametric network summaries

Assessing ChatGPT's Influence on Critical Thinking in Sustainability Oriented Activities

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

A study of ChatGPT in facilitating Heart Team decisions on severe aortic stenosis

Who's Helping Who? When Students Use ChatGPT to Engage in Practice Lab Sessions

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

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

CoBo: Collaborative Learning via Bilevel Optimization

SuperAnimal pretrained pose estimation models for behavioral analysis

Discovering meaningful units from text sequences

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

Explainable ensemble learning model for predicting steel section-concrete bond strength

Neuroimaging Harmonization Using cGANs: Image Similarity Metrics Poorly Predict Cross-Protocol Volumetric Consistency

Unsupervised Controllable Generation with Self-Training

Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines