BACKGROUND: The purpose of this article is to develop a deep learning automatic segmentation model for the segmentation of Crohn's disease (CD) lesions in computed tomography enterography (CTE) images. Additionally, the radiomics features extracted f...
Journal of the Optical Society of America. A, Optics, image science, and vision
Nov 1, 2024
We developed a new method to enhance the resolution of blood platelet aggregates imaged via quantitative phase imaging (QPI) using a Pix2Pix generative adversarial network (GAN). First, 1 µm polystyrene beads were imaged with low- and high-resolution...
OBJECTIVE: To construct a non-invasive pre-hospital screening model and early based on artificial intelligence algorithms to provide the severity of stroke in patients, provide screening, guidance and early warning for stroke patients and their famil...
It has been an open question in deep learning if fault-tolerant computation is possible: can arbitrarily reliable computation be achieved using only unreliable neurons? In the grid cells of the mammalian cortex, analog error correction codes have bee...
Despite deep learning being state of the art for data-driven model predictions, its application in ecology is currently subject to two important constraints: (i) deep-learning methods are powerful in data-rich regimes, but in ecology data are typical...
The Journal of the Acoustical Society of America
Nov 1, 2024
Systems inspired by progressive neural networks, transferring information from end-to-end articulatory feature detectors to similarly structured phone recognizers, are described. These networks, connecting the corresponding recurrent layers of pre-tr...
PURPOSE: Magnetic resonance imaging (MRI) refers to one of the critical image modalities for diagnosis, whereas its long acquisition time limits its application. In this study, the aim was to investigate whether deep learning-based techniques are cap...
For millennia, the ethos of "First, Do No Harm", attributed to Hippocrates, has been a cornerstone of medicine. This principle emphasizes the responsibility and ethical commitment of the clinician to the benefit of their patient. Recently, the rapid ...
PURPOSE: Semantic segmentation is a fundamental part of the surgical application of deep learning. Traditionally, segmentation in vision tasks has been performed using convolutional neural networks (CNNs), but the transformer architecture has recentl...
IEEE journal of biomedical and health informatics
Nov 1, 2024
Sleep staging is a crucial task in sleep monitoring and diagnosis, but clinical sleep staging is both time-consuming and subjective. In this study, we proposed a novel deep learning algorithm named feature fusion temporal convolutional network (FFTCN...
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