Results 191 to 200 of about 9,112 (254)
We utilize a simulation framework including axon map models generated from earlier human trials to predict the neural activation of a novel, origami‐type epiretinal implant. With this, we can assess its expected performance compared to other implants and significantly shorten the development of novel implant concepts in the future.
Eashika Ghosh +6 more
wiley +1 more source
Standardizing DICOM annotation: deep learning enhances body part description in X-ray image retrieval for clinical research. [PDF]
Cheng KY, Fabel M, Bergh B, Saalfeld S.
europepmc +1 more source
ABSTRACT Deep generative models, particularly denoising diffusion models, have achieved remarkable success in high‐fidelity generation of architected microstructures with desired properties and styles. However, these recent methods typically rely on conditional training mechanisms that require extensive labeled data.
Weipeng Xu +5 more
wiley +1 more source
Exploring AI as a Diagnostic Tool in Medical Imaging for Dermatopathological Diseases.
Kakada P +3 more
europepmc +1 more source
MoringaLeafNet: A multi-class leaf disease dataset for precision agriculture and deep learning research. [PDF]
Preanto SA, Paul T, Khan A, Bijoy MHI.
europepmc +1 more source
Abstract Ductal carcinoma in situ (DCIS) spans a biologic continuum from atypical ductal hyperplasia (ADH) to high‐grade lesions with variable risk of progression to invasive ductal carcinoma (IDC), yet morphologic assessment by hematoxylin and eosin (H&E) remains diagnostically limited, particularly at the benign versus ADH/low‐grade DCIS boundary ...
Janghyun Yoo +8 more
wiley +1 more source
A Modular Vision System for Practical Object Detection on Resource-Constrained Humanoid Robots. [PDF]
Lau MC, Pottier N.
europepmc +1 more source
Abstract Background Medical image segmentation is fundamental to radiotherapy planning, yet accurate delineation of organs at risk and tumor targets remain challenging due to anatomical variability and low soft‐tissue contrast in CT images. Purpose To develop a lightweight, high‐precision automatic segmentation network that meets the dual clinical ...
Peijun Yin +6 more
wiley +1 more source
Different BI-RADS breast cancer diagnosis using MobileNetV1 and vision transformer based on explainable artificial intelligence (XAI). [PDF]
Abdelsabour I +3 more
europepmc +1 more source
A multi-class framework for fish species classification using deep learning technique. [PDF]
Farooq Z +5 more
europepmc +1 more source

