Computer-aided diagnostics in digital pathology: automated evaluation of early-phase pancreatic cancer in mice

2014 ◽  
Vol 10 (7) ◽  
pp. 1043-1054 ◽  
Author(s):  
Leeor Langer ◽  
Yoav Binenbaum ◽  
Leonid Gugel ◽  
Moran Amit ◽  
Ziv Gil ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Early Phase ◽  
Digital Pathology ◽  
Automated Evaluation ◽  
Computer Aided

Challenges and Issues in Computer Aided Evaluation of Competences Maturity Degree of Graduating Students Focusing in Various Areas

10.12737/3840 ◽  
2014 ◽  
Vol 2 (2) ◽  
pp. 45-55
Author(s):  
Тигина ◽  
Mariya Tigina
Keyword(s):  
Academic Disciplines ◽  
Evaluation Systems ◽  
Automated Evaluation ◽  
Computer Aided ◽  
Maturity Degree ◽  
Graduating Students ◽  
Knowledge Evaluation

The paper considers the concept of competences and how to develop these in the course of mastering academic disciplines at universities. The need for automated procedure of evaluating degree of competences maturity is emphasized as well as related tasks to be resolved to elaborate such a procedure. Also discussed are current methods of students’ knowledge evaluation, and automated evaluation systems, operating on the basis of these methods.

Computer Aided Semi-Automated Evaluation of HER2 Immunodetection—A Robust Solution for Supporting the Accuracy of Anti HER2 Therapy

2015 ◽  
Vol 21 (4) ◽  
pp. 1005-1011 ◽  
Author(s):  
Tamás Micsik ◽  
Gábor Kiszler ◽  
Daniel Szabó ◽  
László Krecsák ◽  
Csaba Hegedűs ◽  
...  
Keyword(s):  
Robust Solution ◽  
Automated Evaluation ◽  
Computer Aided

scholarly journals Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 141705-141718
Author(s):  
Min Li ◽  
Xiaohan Nie ◽  
Yilidan Reheman ◽  
Pan Huang ◽  
Shuailei Zhang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

An Early Phase II Study of S-1 in Patients with Metastatic Pancreatic Cancer

Oncology ◽  
2005 ◽  
Vol 68 (2-3) ◽  
pp. 171-178 ◽  
Author(s):  
Hideki Ueno ◽  
Takuji Okusaka ◽  
Masafumi Ikeda ◽  
Yoriko Takezako ◽  
Chigusa Morizane
Keyword(s):  
Pancreatic Cancer ◽  
Phase Ii ◽  
Early Phase ◽  
Phase Ii Study ◽  
Metastatic Pancreatic Cancer

scholarly journals Nuclei segmentation for computer-aided diagnosis of breast cancer

2014 ◽  
Vol 24 (1) ◽  
pp. 19-31 ◽  
Author(s):  
Marek Kowal ◽  
Paweł Filipczuk
Keyword(s):  
Breast Cancer ◽  
Color Space ◽  
Digital Pathology ◽  
Breast Cancer Diagnosis ◽  
Computer Aided Diagnosis ◽  
Medical Decision ◽  
Fast Marching ◽  
Fine Needle ◽  
Computer Aided ◽  
Aided Diagnosis

Abstract Breast cancer is the most common cancer among women. The effectiveness of treatment depends on early detection of the disease. Computer-aided diagnosis plays an increasingly important role in this field. Particularly, digital pathology has recently become of interest to a growing number of scientists. This work reports on advances in computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies. The task at hand is to classify those as either benign or malignant. We propose a robust segmentation procedure giving satisfactory nuclei separation even when they are densely clustered in the image. Firstly, we determine centers of the nuclei using conditional erosion. The erosion is performed on a binary mask obtained with the use of adaptive thresholding in grayscale and clustering in a color space. Then, we use the multi-label fast marching algorithm initialized with the centers to obtain the final segmentation. A set of 84 features extracted from the nuclei is used in the classification by three different classifiers. The approach was tested on 450 microscopic images of fine needle biopsies obtained from patients of the Regional Hospital in Zielona Góra, Poland. The classification accuracy presented in this paper reaches 100%, which shows that a medical decision support system based on our method would provide accurate diagnostic information.

scholarly journals Computer Aided Small Molecule Modulation of Pancreatic Cancer Mechanobiology

2019 ◽  
Vol 116 (3) ◽  
pp. 415a
Author(s):  
Kathleen T. DiNapoli ◽  
Eric Schiffhauer ◽  
Alexandra Surcel ◽  
Dustin Thomas ◽  
Pablo Iglesias ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Small Molecule ◽  
Computer Aided

A novel Phase I/IIa design for early phase oncology studies and its application in the evaluation of MK-0752 in pancreatic cancer

2012 ◽  
Vol 31 (18) ◽  
pp. 1931-1943 ◽  
Author(s):  
John Whitehead ◽  
Helene Thygesen ◽  
Thomas Jaki ◽  
Scot Davies ◽  
Sarah Halford ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Phase I ◽  
Early Phase

scholarly journals Novel Computer Aided Diagnostic System Using Synergic Deep Learning Technique for Early Detection of Pancreatic Cancer

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 02) ◽  
pp. 367-379
Author(s):  
Sabah Khudhair Abbas ◽  
Rusul. Sabah. Obied
Keyword(s):  
Pancreatic Cancer ◽  
Deep Learning ◽  
Network Architecture ◽  
Research Work ◽  
The Other ◽  
Training Data ◽  
Cell Multiplication ◽  
Suggested Approach ◽  
Cancer Pathogenesis ◽  
Computer Aided

Pancreatic cancer (PC) in the more extensive sense alludes to in excess of 277 distinct kinds of cancer sickness. Researchers have recognized distinctive phase of pancreatic cancers, showing that few quality transformations are engaged with cancer pathogenesis. These quality transformations lead to unusual cell multiplication. Therefore, in this study we propose a Computer Aided Diagnosis (CAD) system using Synergic Inception ResNet-V2, Deep convoluted neural network architecture for the identification of PC cases from publically Usable CT images that could extract PC graphical functionality to include clinical diagnosis before the pathogenic examination, saving valuable time for disease prevention. Simulation results using MATLAB is shown to illustrate that quite promising results have been obtained in terms of accuracy in detecting patients infected with BC. Accuracy of 99.23 per cent is reached using the proposed deep learning method, which is better than all other state-of-the-art approaches available in the literature. The calculation time was found to be less than the other current 22 second process. The proximity of the suggested approach to the True Positive values in the ROC curve suggests a result that is greater than the other methods. The comparative study with Inception ResNet-V2 is based on separate test and training data at a rate of 90 percent-10 percent, 80 percent-20 percent and 70 percent-30% respectively, which shows the robustness of the proposed research work. Experimental findings show the proposed reliability of the device relative to other detection approaches. The proposed CAD device is fully automated and has thus proved to be a promising additional diagnostic tool for frontline clinical physicians.

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