Latest AI and machine learning research in surveys for healthcare professionals.
Ensuring reliability is paramount in deep learning, particularly within the domain of medical imaging, where diagnostic decisions often hinge on model outputs. The capacity to separate out-of-distribution (OOD) samples has proven to be a valuable indicator of a model's reliability in research. In medical imaging, this is especially critical, as identifying OOD inputs can help flag potential anom...
Culturally adaptive emotional responses remain a critical challenge in affective computing. This paper introduces Affective-CARA, an agentic framework designed to enhance user-agent interactions by integrating a Cultural Emotion Knowledge Graph (derived from StereoKG) with Valence, Arousal, and Dominance annotations, culture-specific data, and cross-cultural checks to minimize bias. A Gradient-B...
The integration of Large Language Models (LLMs) with computer vision is profoundly transforming perception tasks like image segmentation. For intell...
Conducting disparity assessments at regular time intervals is critical for surfacing potential biases in decision-making and improving outcomes acro...
This paper comprehensively surveys research trends in imitation learning for contact-rich robotic tasks. Contact-rich tasks, which require complex p...
Recent advancements in diffusion-based text-to-image (T2I) models have enabled the generation of high-quality and photorealistic images from text de...
Person Re-identification (ReID) aims to retrieve images of the same individual captured across non-overlapping camera views, making it a critical co...
Case Report Forms (CRFs) are largely used in medical research as they ensure accuracy, reliability, and validity of results in clinical studies. How...
During prediction tasks, models can use any signal they receive to come up with the final answer - including signals that are causally irrelevant. W...
Modern language models are trained on large amounts of data. These data inevitably include controversial and stereotypical content, which contains a...
We consider the problem of generalizable novel view synthesis (NVS), which aims to generate photorealistic novel views from sparse or even unposed 2...
Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing, by i...
(MAB) infections pose a significant treatment challenge due to their intrinsic resistance to antibiotics, requiring prolonged multidrug regimens with...
The rapid adoption of generative AI models in domains such as education, policing, and social media raises significant concerns about potential bias...
Metaphors are pervasive in communication, making them crucial for natural language processing (NLP). Previous research on automatic metaphor process...
To build fair AI systems we need to understand how social-group biases intrinsic to foundational encoder-based vision-language models (VLMs) manifes...
Diagnosing deep neural networks (DNNs) through the eigenspectrum of weight matrices has been an active area of research in recent years. At a high l...
Segmentation of lung gross tumour volumes is an important first step in radiotherapy and surgical intervention, and is starting to play a role in as...
As one of the first research teams with full access to Siemens' Cinematic Reality, we evaluate its usability and clinical potential for cinematic vo...
Image matching, which establishes correspondences between two-view images to recover 3D structure and camera geometry, serves as a cornerstone in co...