Latest AI and machine learning research in adhd/add for healthcare professionals.
Cervical cancer is a malignant tumor that endangers women's life and health. While deep learning has enhanced the accuracy of cervical cell classification, there remain obstacles impeding further performance enhancement, including the similarities between different categories, variability between single cells and cell clusters, as well as the accuracy of annotations. To address these issues, a nov...
BACKGROUND: Attention-Deficit-Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental disorder that impacts cognitive control processes. While neurophysiological data (e.g., EEG data) have provided valuable insights into its underlying mechanisms, fully understanding the altered cognitive functions in ADHD requires advanced analytical approaches capable of capturing the highly dimension...
Colorectal cancer is one of the deadliest cancers today, but it can be prevented through early detection of malignant polyps in the colon, primarily...
Image steganography is a technique that conceals secret information in a cover image to achieve covert communication. Recent research has demonstrat...
The task of scene text editing is to modify or add texts on images while maintaining the fidelity of newly generated text and visual coherence with ...
Automatic segmentation of anatomical landmarks in endoscopic images can provide assistance to doctors and surgeons for diagnosis, treatments or medi...
Triage is used in emergency departments to ensure timely patient care according to urgency of treatment. However, triage accuracy and efficiency remai...
BACKGROUND: Good medical records are an essential part of healthcare. However, the burden of clinical documentation can reduce clinician productivity ...
This study systematically tests a computational power reuse scheme proposed by the open source community disabling specific instruction sets (Fused ...
Labeling errors in remote sensing (RS) image segmentation datasets often remain implicit and subtle due to ambiguous class boundaries, mixed pixels,...
Convolutional neural network (CNN) slides a kernel over the whole image to produce an output map. This kernel scheme reduces the number of parameter...
As centralized social media platforms face growing concerns, more users are seeking greater control over their social feeds and turning to decentral...
As extended reality (XR) is redefining how users interact with computing devices, research in human action recognition is gaining prominence. Typica...
Perceptual hashing is used to detect whether an input image is similar to a reference image with a variety of security applications. Recently, they ...
Text-to-image models based on diffusion processes, such as DALL-E, Stable Diffusion, and Midjourney, are capable of transforming texts into detailed...
The rapid advancement of diffusion models and personalization techniques has made it possible to recreate individual portraits from just a few publi...
Presentation Attack Detection (PAD) systems are usually designed independently of the fingerprint verification system. While this can be acceptable ...
Neuro-developmental disorders are manifested as dysfunctions in cognition, communication, behaviour and adaptability, and deep learning-based comput...
For effective image segmentation, it is crucial to employ constraints informed by prior knowledge about the characteristics of the areas to be segme...
In modern society, Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the common mental diseases discovered not only in children but also in ...