Ruyi Ding (Northeastern University), Tong Zhou (Northeastern University), Lili Su (Northeastern University), Aidong Adam Ding (Northeastern University), Xiaolin Xu (Northeastern University), Yunsi Fei ...
Hyderabad: Artificial Intelligence (AI) is transforming the way sleep disorders are diagnosed, with researchers at the ...
Tesla Full Self-Driving (Supervised) v14 has received uncharacteristically vocal support from one of the most esteemed ...
Objective: Deep learning (DL) has introduced new possibilities for estimating human joint moments - a surrogate measure of joint loads. However, traditional methods typically require extensive ...
Abstract: This work investigates a method for self-supervised neural network training for moving target indicator radar in order to replace or augment conventional algorithms such as space time ...
This important work presents a self-supervised method for the segmentation of 3D cells in fluorescent microscopy images, conveniently packaged as a Napari plugin and tested on an annotated dataset.
In PhotoniX, researchers report a self-supervised deep learning method that denoises dynamic fluorescence images in vivo without requiring clean training data. The figure shows in vivo venule images ...
Larotrectinib resistance in TRK fusion cancers: Analysis of a tumor-agnostic, global clinical trial dataset. This is an ASCO Meeting Abstract from the 2025 ASCO Annual Meeting I. This abstract does ...
Objectives: Oral cavity-derived cancer pathological images (OPI) are crucial for diagnosing oral squamous cell carcinoma (OSCC), but existing deep learning methods for OPI segmentation rely heavily on ...
Abstract: The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges, particularly in device authentication and identity verification. Traditional ...
ABSTRACT: The ever-increasing use of IoT devices has presented new security threats and, thus, requires IDS to protect interconnected IoT networks. This paper identifies the use of ML and DL as a new ...