Iteratively training classifiers for circulating tumor cell detection

Proceedings - International Symposium on Biomedical Imaging

Volumn 2015-July, Issue , 2015, Pages 190-194

  Mao, Yunxiang ^a   Yin, Zhaozheng ^a   Schober, Joseph M ^b  

^a MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY   (United States)

^b SOUTHERN ILLINOIS UNIVERSITY EDWARDSVILLE   (United States)

Author keywords

circulating tumor cells; convolutional neural network; iterative training; support vector machine

Indexed keywords

BLOOD; CELLS; CLASSIFICATION (OF INFORMATION); CONVOLUTION; CONVOLUTIONAL NEURAL NETWORKS; DIAGNOSIS; MEDICAL IMAGING; SUPPORT VECTOR MACHINES; TUMORS;

CHEMOTHERAPEUTIC AGENTS; CIRCULATING TUMOR CELLS; CLASSIFICATION BOUNDARY; DISEASE PROGRESSION; HISTOGRAMS OF ORIENTED GRADIENTS (HOG); ROUTINE DETECTIONS; TRAINING ALGORITHMS; TREATMENT MONITORING;

ITERATIVE METHODS;

EID: 84944315641     PISSN: 19457928     EISSN: 19458452     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2015.7163847     Document Type: Conference Paper

References (14)

1. Emilian Racila et al., Detection and characterization of carcinoma cells in the blood, In Proceedings of the National Academy of Sciences, vol. 95, no. 8, pp. 4589-4594, 1998
2. Tanja Fehm et al., Cytogenetic evidence that circulating epithelial cells in patients with carcinoma are malignant, Clinical Cancer Research, vol. 8, no. 7, pp. 2073-2084, 2002.
3. Jeffrey Allard et al., Tumor cells circulate in the peripheral blood of all major carcinomas but not in healthy subjects or patients with nonmalignant diseases, Clinical Cancer Research, vol. 10, no. 20, pp. 6897-6904, 2004.
4. Klaus Pantel et al., Detection, clinical relevance and specific biological properties of disseminating tumour cells, Nature Reviews Cancer, vol. 8, no. 5, pp. 329-340, 2008.
5. Philip TH Went et al., Frequent epcam protein expression in human carcinomas, Human Pathology, vol. 35, no. 1, pp. 122-128, 2004.
6. Robert Königsberg et al., Detection of epcam positive and negative circulating tumor cells in metastatic breast cancer patients, Acta Oncologica, vol. 50, no. 5, pp. 700-710, 2011.
7. Udo Bilkenroth et al., Detection and enrichment of disseminated renal carcinoma cells from peripheral blood by immunomagnetic cell separation, International Journal of Cancer, vol. 92, no. 4, pp. 577-582, 2001.
8. Esmaeilsabzali Hadi et al., Detection and isolation of circulating tumor cells: Principles and methods, Biotechnology Advances, vol. 31, no. 7, pp. 1063-1084, 2013.
9. Navneet Dalal and Bill Triggs, Histograms of oriented gradients for human detection, in IEEE CVPR, vol. 1, pp. 886-893, 2005
10. Corinna Cortes and Vladimir Vapnik, Support-vector networks, Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
11. Dan C Ciresan et al., Mitosis detection in breast cancer histology images with deep neural networks, in Medical Image Computing and Computer-Assisted Intervention, pp. 411-418, Springer, 2013.
12. Mehdi Habibzadeh et al., White blood cell differential counts using convolutional neural networks for low resolution images, in Artificial Intelligence and Soft Computing, pp. 263-274, Springer, 2013
13. Ting Chen and Christophe Chefdhotel, Deep learning based automatic immune cell detection for immunohistochemistry images, in 5th International Workshop, MLMI, pp. 17-24, Springer, 2014.
14. Mark Everingham et al., The pascal visual object classes (voc) challenge, International Journal of Computer Vision, vol. 88, no. 2, pp. 303-338, 2010.