OBJECTIVES: Robot-assisted laparoscopic surgeries (RLSs) have become increasingly common in the past decade alongside conventional laparoscopic surgeries (CLSs). In general, RLSs have been reported to be superior to CLSs; therefore, we compared both ...
A 65-year-old male with disseminated prostate cancer and newly diagnosed colonic cancer underwent elective robotic right hemicolectomy with intracorporeal anastomosis and had an uncomplicated short-term postoperative course. More than two years after...
MOTIVATION: Digital pathology supports analysis of histopathological images using deep learning methods at a large-scale. However, applications of deep learning in this area have been limited by the complexities of configuration of the computational ...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Nov 1, 2021
Fast and accurate cancer prognosis stratification models are essential for treatment designs. Large labeled patient data can power advanced deep learning models to obtain precise predictions. However, since fully labeled patient data are hard to acqu...
BACKGROUND: Identifying genetic mutations in cancer patients have been increasingly important because distinctive mutational patterns can be very informative to determine the optimal therapeutic strategy. Recent studies have shown that deep learning-...
Gan to kagaku ryoho. Cancer & chemotherapy
Dec 1, 2019
No large clinical trials have been conducted to prove the efficacy of peritoneal dissemination resection for colorectal cancer, and no evidence has shown the usefulness of resection for metachronous peritoneal dissemination. An elderly woman in her 7...
To better understand the capabilities and challenges of artificial intelligence and machine learning, we look at the role they can play in screening for retinopathy and colon cancer.
Mathematical biosciences and engineering : MBE
Sep 30, 2019
Recently, Yang et al. (2019) proposed a fuzzy model-based Gaussian (F-MB-Gauss) clustering that combines a model-based Gaussian with fuzzy membership functions for clustering. In this paper, we further consider the F-MB-Gauss clustering with the leas...
MOTIVATION: One of the main challenges in machine learning (ML) is choosing an appropriate normalization method. Here, we examine the effect of various normalization methods on analyzing FPKM upper quartile (FPKM-UQ) RNA sequencing data sets. We coll...