Application of Deep Learning to Pancreatic Cancer Detection: Lessons Learned From Our Initial Experience

2019 ◽  
Vol 16 (9) ◽  
pp. 1338-1342 ◽  
Author(s):  
Linda C. Chu ◽  
Seyoun Park ◽  
Satomi Kawamoto ◽  
Yan Wang ◽  
Yuyin Zhou ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Lessons Learned ◽  
Initial Experience

231 PANCREAS SEGMENTATION AND PANCREATIC CANCER DETECTION ON CT WITH DEEP LEARNING

2021 ◽  
Vol 160 (6) ◽  
pp. S-50
Author(s):  
Wei-Chih Liao ◽  
Po-Ting Chen ◽  
Tinghui Wu ◽  
Dawei Chang ◽  
Pochuang Wang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Pancreas Segmentation

Systems Biology Approaches to Pancreatic Cancer Detection, Prevention and Treatment

2014 ◽  
Vol 20 (1) ◽  
pp. 73-80 ◽  
Author(s):  
Osama Alian ◽  
Philip Philip ◽  
Fazlul Sarkar ◽  
Asfar Azmi
Keyword(s):  
Pancreatic Cancer ◽  
Systems Biology ◽  
Cancer Detection ◽  
Prevention And Treatment

scholarly journals Quality control stress test for deep learning-based diagnostic model in digital pathology

2021 ◽  
Author(s):  
Birgid Schömig-Markiefka ◽  
Alexey Pryalukhin ◽  
Wolfgang Hulla ◽  
Andrey Bychkov ◽  
Junya Fukuoka ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cancer Detection ◽  
Stress Testing ◽  
Computational Analysis ◽  
Stress Test ◽  
Digital Pathology ◽  
Model Performance ◽  
Diagnostic Model ◽  
Model Accuracy ◽  
Diagnostic Models

AbstractDigital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections’ thickness, and artifacts arising during tissue processing, cutting, staining, and digitization. In this study, we digitally reproduce major types of artifacts. Using six datasets from four different institutions digitized by different scanner systems, we systematically explore artifacts’ influence on the accuracy of the pre-trained, validated, deep learning-based model for prostate cancer detection in histological slides. We provide evidence that any histological artifact dependent on severity can lead to a substantial loss in model performance. Strategies for the prevention of diagnostic model accuracy losses in the context of artifacts are warranted. Stress-testing of diagnostic models using synthetically generated artifacts might be an essential step during clinical validation of deep learning-based algorithms.

scholarly journals Deep Learning Based Lung Cancer Detection and Classification

2020 ◽  
Vol 994 ◽  
pp. 012026
Author(s):  
N Kalaivani ◽  
N Manimaran ◽  
Dr. S Sophia ◽  
D D Devi
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Lung Cancer Detection

scholarly journals Prostate Cancer Detection Using Deep Learning and Traditional Techniques

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 27085-27100
Author(s):  
Saqib Iqbal ◽  
Ghazanfar Farooq Siddiqui ◽  
Amjad Rehman ◽  
Lal Hussain ◽  
Tanzila Saba ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Prostate Cancer Detection
Sign in / Sign up

Export Citation Format

Share Document