Latest AI and machine learning research in adhd/add for healthcare professionals.
Self-supervised learning is increasingly investigated for low-dose computed tomography (LDCT) image denoising, as it alleviates the dependence on paired normal-dose CT (NDCT) data, which are often difficult to acquire in clinical practice. In this paper, we propose a novel self-supervised training strategy that relies exclusively on LDCT images. We introduce a step-wise blind-spot denoising mechan...
Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder with limited objective diagnostic tools, highlighting the urgent need for objective, biology-based diagnostic frameworks in precision psychiatry. We integrate urinary metabolomics with an interpretable machine learning framework to identify biochemical signatures associated with ADHD. Targeted metabolomic pr...
Accurate segmentation of polyps from colonoscopy images is crucial for the early diagnosis and treatment of colorectal cancer. Most existing deep le...
Accurately converting pixel measurements into absolute real-world dimensions remains a fundamental challenge in computer vision and limits progress ...
The data extraction stages of reviews are resource-intensive, and researchers may seek to expediate data extraction using online (large language mod...
Recently, research into chatbots (also known as conversational agents, AI agents, voice assistants), which are computer applications using artificia...
Document shadow removal is a crucial task in the field of document image enhancement. However, existing methods tend to remove shadows with constant...
In the past, the chest X-ray (CXR) was a traditional age and amount requirement used to assess potential mortality risk in life insurance applicants. ...
Depth map enhancement using paired high-resolution RGB images offers a cost-effective solution for improving low-resolution depth data from lightwei...
Small object detection in UAV imagery is crucial for applications such as search-and-rescue, traffic monitoring, and environmental surveillance, but...
Optical computing and spiking neural networks (SNNs) have garnered significant attention as next-generation technologies due to their high parallelism...
Knowledge distillation is a model compression technique in which a compact "student" network is trained to replicate the predictive behavior of a la...
This paper addresses two main objectives. Firstly, we demonstrate the impressive performance of the LLaVA-NeXT-interleave on 22 datasets across thre...
Dual encoder Vision-Language Models (VLM) such as CLIP are widely used for image-text retrieval tasks. However, those models struggle with compositi...
Data in the form of images or higher-order tensors is ubiquitous in modern deep learning applications. Owing to their inherent high dimensionality, ...
The importance of clinical variables in the prognosis of the disease is explained using statistical correlation or machine learning (ML). However, t...
Automated respiratory sound classification faces practical challenges from background noise and insufficient denoising in existing systems. We pro...
Multimodal artificial intelligence (AI) is a powerful new technological advance, capable of simultaneously learning from diverse data types, such as t...
Artificial intelligence in radiology critically depends on vast amounts of quality data, and there are controversies surrounding the topic of data own...
Rapid spread of false images and videos on online platforms is an emerging problem. Anyone may add, delete, clone or modify people and entities from...