An application of transfer learning to neutrophil cluster detection for tuberculosis: efficient implementation with nonmetric multidimensional scaling and sampling

M. Khalid Khan Niazi; Gillian Beamer; Metin N. Gurcan

doi:10.1117/12.2292249

6 March 2018 An application of transfer learning to neutrophil cluster detection for tuberculosis: efficient implementation with nonmetric multidimensional scaling and sampling

M. Khalid Khan Niazi, Gillian Beamer, Metin N. Gurcan

Author Affiliations +

Proceedings Volume 10581, Medical Imaging 2018: Digital Pathology; 1058108 (2018) https://doi.org/10.1117/12.2292249
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

A neutrophil is a type of white blood cell that is responsible for killing pathogenic bacteria but may simultaneously damage host tissue. We established a method to automatically detect neutrophils from slides stained with hematoxylin and eosin (H and E), because there is growing evidence that neutrophils, which respond to Mycobacterium tuberculosis, are cellular biomarkers of lung damage in tuberculosis. The proposed method relies on transfer learning to reuse features extracted from the activation of a deep convolutional network trained on a large dataset. We present a methodology to identify the correct tile size, magnification, and the number of tiles using multidimensional scaling to efficiently train the final layer of this pre-trained network. The method was trained on tiles acquired from 12 whole slide images, resulting in an average accuracy of 93.0%. The trained system successfully identified all neutrophil clusters on an independent dataset of 53 images. The method can be used to automatically, accurately, and efficiently count the number of neutrophil sites in regionsof-interest extracted from whole slide images.

Citation Download Citation

M. Khalid Khan Niazi, Gillian Beamer, and Metin N. Gurcan "An application of transfer learning to neutrophil cluster detection for tuberculosis: efficient implementation with nonmetric multidimensional scaling and sampling", Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 1058108 (6 March 2018); https://doi.org/10.1117/12.2292249

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