Digital pathology: Multiple instance learning can detect Barrett's cancer

2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Volumn , Issue , 2014, Pages 1348-1351

  Kandemir, Melih ^a   Feuchtinger, Annette ^b   Walch, Axel ^b   Hamprecht, Fred A ^a  

^a UNIVERSITY OF HEIDELBERG   (Germany)

^b INSTITUTE OF EPIDEMIOLOGY   (Germany)

Author keywords

Cancer diagnosis; Histopathological tissue imaging; Multiple instance learning

Indexed keywords

BENCHMARKING; DISEASES; GRAPHIC METHODS; LEARNING SYSTEMS; MEDICAL IMAGING; TEXTURES; TISSUE;

CANCER DIAGNOSIS; COLOR AND TEXTURE FEATURES; DIGITAL PATHOLOGIES; FEATURE VECTORS; LOGISTIC REGRESSIONS; MULTIPLE INSTANCE LEARNING; RECTANGULAR PATCH; TISSUE IMAGING;

E-LEARNING;

EID: 84927940292     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/isbi.2014.6868127     Document Type: Conference Paper

References (10)

1. Z.H. Zhou et al., "Multi-instance learning by treating instances as non-I.I.D. samples," Proc. Int'l Conf. Machine Learning, pp. 1-8, 2009.
2. M.N. Gurcan et al., "Histopathological image analysis: a review.," IEEE Reviews in Biomedical Engineering, Vol. 2, pp. 147-71, Jan. 2009.
3. D.F. Gleason, "Histologic grading of prostate cancer: a perspective," Human pathology, Vol. 23, no. 3, pp. 273-279, 1992.
4. C.W. Wang, "Robust automated tumour segmentation on histological and immunohistochemical tissue images," PLoS ONE, Vol. 6, no. 2, pp. e15818, 2011.
5. P.W. Huang and C.H. Lee, "Automatic classification for pathological prostate images based on fractal analysis.," IEEE Trans. Medical Imaging, Vol. 28, no. 7, pp. 1037-50, 2009.
6. R. Langer et al., "Assessment of ErbB2 (Her2) in oe-sophageal adenocarcinomas: summary of a revised im-munohistochemical evaluation system, bright field double in situ hybridisation and fluorescence in situ hybridisation," Modern Pathology, Vol. 24, no. 7, pp. 908-916, 2011.
7. C. Sommer et al., ""Ilastik: Interactive learning and segmentation toolkit"," in Int'l Symposium on Biomedical Imaging, 2011.
8. O. Maron et al., "A framework for multiple-instance learning," Advances in Neural Information Processing Systems, pp. 570-576, 1998.
9. P. Soille, Morphological image analysis: principles and applications, Springer-Verlag New York, Inc., 2003.
10. S. Andrews et al., "Support vector machines for multiple-instance learning," in Advances in Neural Information Processing Systems, 2003.