Integrating a deep neural network and Transformer architecture for the automatic segmentation and survival prediction in cervical cancer.

Background: Automated tumor segmentation and survival prediction are critical to clinical diagnosis and treatment. This study aimed to develop deep-learning models for automatic tumor segmentation and survival prediction in magnetic resonance imaging (MRI) of cervical cancer (CC) by combining deep neural networks and Transformer architecture.

Methods: This study included 406 patients with CC, each with comprehensive clinical information and MRI scans.

Improved night shift schedule related to the mortality of critically ill patients with Corona Virus Disease 2019.

Purpose: To determine the relationship between the improved night shift schedule and the mortality of critically ill patients with Corona Virus Disease 2019 (COVID-19).

Methods: According to the time of the implementation of the new night shift schedule, we divided all patients into two groups: initial period group and recent period group. The clinical electronic medical records, nursing records, laboratory findings, and radiological examinations for all patients with laboratory confirmed Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection were reviewed.

Frequency-Diverse Bunching Metamaterial Antenna for Coincidence Imaging.

A frequency-diverse bunching metamaterial antenna for coincidence imaging in the Ka band is proposed in this paper. The bunching metamaterial antenna includes a broadband circular array and a frequency-diverse bunching metalens. Firstly, in order to enhance the bunching characteristic, the broadband circular array is designed based on the 60-degree beamwidth design to generate radiation patterns from 32 GHz to 36 GHz.

Multi-polarization, polarization-independent, wide-angle RCS reduction metasurface based on random phase gradients.

A novel metasurface based on random phase gradients is proposed for radar cross-section (RCS) reduction. In this work, wideband, polarization-independent, wide-angle RCS reduction is realized for both circularly polarized (CP) waves and linearly polarized (LP) waves, respectively. Thus, true polarization-independent RCS reduction is realized by the proposed metasurface.

Self-Adaption Matched Filter and Bi-Directional Difference Method for Moving Target Detection.

In this paper, a self-adaption matched filter (SMF) and bi-directional difference techniques are proposed to detect a small moving target in urban environments. Firstly, the SMF technique is proposed to improve the signal-to-interference-noise ratio (SINR) by using the power factor. The properties of the transmitting signal, the target echoes and the interference and noise are considered during the power factor generation.

Ghost Imaging Based on Deep Learning.

Even though ghost imaging (GI), an unconventional imaging method, has received increased attention by researchers during the last decades, imaging speed is still not satisfactory. Once the data-acquisition method and the system parameters are determined, only the processing method has the potential to accelerate image-processing significantly. However, both the basic correlation method and the compressed sensing algorithm, which are often used for ghost imaging, have their own problems.