Using Deep Neural Network to Diagnose Thyroid Nodules on Ultrasound in Patients With Hashimoto's Thyroiditis.

Objective: The aim of this study is to develop a model using Deep Neural Network (DNN) to diagnose thyroid nodules in patients with Hashimoto's Thyroiditis.

Methods: In this retrospective study, we included 2,932 patients with thyroid nodules who underwent thyroid ultrasonogram in our hospital from January 2017 to August 2019. 80% of them were included as training set and 20% as test set.

Using BI-RADS Stratifications as Auxiliary Information for Breast Masses Classification in Ultrasound Images.

Breast Ultrasound (BUS) imaging has been recognized as an essential imaging modality for breast masses classification in China. Current deep learning (DL) based solutions for BUS classification seek to feed ultrasound (US) images into deep convolutional neural networks (CNNs), to learn a hierarchical combination of features for discriminating malignant and benign masses. One existing problem in current DL-based BUS classification was the lack of spatial and channel-wise features weighting, which inevitably allow interference from redundant features and low sensitivity.

Attenuated total reflection Fourier transform infrared spectroscopic investigation of the postmortem metabolic process in rat and human kidney cortex.

Attenuated total reflection (ATR) Fourier transform infrared (FT-IR) spectroscopy has been applied to study the short and long term postmortem metabolic processes in rat and human kidney cortexes. The goals of this project were as follows: (1) to investigate the changes of ATR spectra in different rat and human tissues after death, (2) to explore the best mathematical model with different band absorption ratio changes to determine the postmortem interval (PMI), and (3) to establish a preliminary human postmortem ATR spectra database. There were three different types of metabolic changes after death based on the spectral results: (1) the intensities of some bands increased continuously (e.