How to differentiate thyroid cancer nodules from a large number of benign nodules is always a challenging subject for clinicians. This paper proposes a novel Sal-deel network model to achieve the classification and diagnosis of thyroid cancer, which ...
We use Raman microscopic images with high spatial and spectral resolution to investigate differences between human follicular thyroid (Nthy-ori 3-1) and follicular thyroid carcinoma (FTC-133) cells, a well-differentiated thyroid cancer. Through compa...
BACKGROUND: There is ongoing debate about whether or not robot-assisted thyroidectomy is appropriate for modified radical neck dissection (MRND). The purpose of this study was to compare the surgical outcomes of robot-assisted MRND with those of a co...
Increasing evidence has indicated that microRNAs(miRNAs) play vital roles in various pathological processes and thus are closely related with many complex human diseases. The identification of potential disease-related miRNAs offers new opportunities...
Journal of laparoendoscopic & advanced surgical techniques. Part A
Mar 21, 2019
BACKGROUND: In the past 20 years, the fast spread of new surgical technologies has reached an important peak with the advent of the robotic surgery. Many studies have been run about a cosmetic desire to avoid neck scars after thyroid surgery and this...
PURPOSE: To develop a deep learning-based computer-aided diagnosis (CAD) system for use in the CT diagnosis of cervical lymph node metastasis (LNM) in patients with thyroid cancer.
Diagnostic and interventional imaging
Mar 15, 2019
PURPOSE: The goal of this data challenge was to create a structured dynamic with the following objectives: (1) teach radiologists the new rules of General Data Protection Regulation (GDPR), while building a large multicentric prospective database of ...
Diagnostic and interventional imaging
Feb 25, 2019
PURPOSE: The purpose of this study was to evaluate the performance of a deep learning algorithm in detecting abnormalities of thyroid cartilage from computed tomography (CT) examination.
BACKGROUND: We designed a deep convolutional neural network (CNN) to diagnose thyroid malignancy on ultrasound (US) and compared the diagnostic performance of CNN with that of experienced radiologists.
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