scholarly journals Deep convolutional neural network Inception-v3 model for differential diagnosing of lymph node in cytological images: a pilot study

2019 ◽  
Vol 7 (14) ◽  
pp. 307-307
Author(s):  
Qing Guan ◽  
Xiaochun Wan ◽  
Hongtao Lu ◽  
Bo Ping ◽  
Duanshu Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Lymph Node ◽  
Pilot Study ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network

scholarly journals Deep Convolutional Neural Network-Based Lymph Node Metastasis Prediction for Colon Cancer Using Histopathological Images

2021 ◽  
Vol 10 ◽  
Author(s):  
Min Seob Kwak ◽  
Hun Hee Lee ◽  
Jae Min Yang ◽  
Jae Myung Cha ◽  
Jung Won Jeon ◽  
...  
Keyword(s):  
Neural Network ◽  
Colon Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Cancer Tissue ◽  
Negative Group ◽  
Positive Group ◽  
Node Metastasis

BackgroundHuman evaluation of pathological slides cannot accurately predict lymph node metastasis (LNM), although accurate prediction is essential to determine treatment and follow-up strategies for colon cancer. We aimed to develop accurate histopathological features for LNM in colon cancer.MethodsWe developed a deep convolutional neural network model to distinguish the cancer tissue component of colon cancer using data from the tissue bank of the National Center for Tumor Diseases and the pathology archive at the University Medical Center Mannheim, Germany. This model was applied to whole-slide pathological images of colon cancer patients from The Cancer Genome Atlas (TCGA). The predictive value of the peri-tumoral stroma (PTS) score for LNM was assessed.ResultsA total of 164 patients with stages I, II, and III colon cancer from TCGA were analyzed. The mean PTS score was 0.380 (± SD = 0.285), and significantly higher PTS scores were observed in patients in the LNM-positive group than those in the LNM-negative group (P < 0.001). In the univariate analyses, the PTS scores for the LNM-positive group were significantly higher than those for the LNM-negative group (P < 0.001). Further, the PTS scores in lymphatic invasion and any one of perineural, lymphatic, or venous invasion were significantly increased in the LNM-positive group (P < 0.001 and P < 0.001).ConclusionWe established the PTS score, a simplified reproducible parameter, for predicting LNM in colon cancer using computer-based analysis that could be used to guide treatment decisions. These findings warrant further confirmation through large-scale prospective clinical trials.

scholarly journals Deep convolutional neural network applied to the liver imaging reporting and data system (LI-RADS) version 2014 category classification: a pilot study

2019 ◽  
Vol 45 (1) ◽  
pp. 24-35 ◽  
Author(s):  
Rikiya Yamashita ◽  
Amber Mittendorf ◽  
Zhe Zhu ◽  
Kathryn J. Fowler ◽  
Cynthia S. Santillan ◽  
...  
Keyword(s):  
Neural Network ◽  
Pilot Study ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Data System ◽  
Liver Imaging

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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