Comparison of Different Classifiers with Active Learning to Support Quality Control in Nucleus Segmentation in Pathology Images

By Si Wen, Tahsin M. Kurç, Le Hou, Joel H. Saltz, Rajarsi R. Gupta, Rebecca C. Batiste, Tianhao Zhao, Vu Nguyen, Dimitris Samaras, Wei Zhu. 2018

48 citations recorded by the source corpus.

Segmentation of nuclei in whole slide tissue images is a common methodology in pathology image analysis. Most segmentation algorithms are sensitive to input algorithm parameters and the characteristics of input images (tissue morphology, staining, etc.). Because there can be large variability in the color, texture, and morphology of tissues within and across cancer types (heterogeneity can exist even within a tissue specimen), it is likely that a set of input parameters will not perform well across multiple images. It is, therefore, highly desired, and necessary in some cases, to carry out a quality control of segmentation results. This work investigates the application of machine learning in this process. We report on the application of active learning for segmentation quality assessment for pathology images and compare three classification methods, Support Vector Machine (SVM), Random Forest (RF) and Convolutional Neural Network (CNN), for their performance improvement and efficiency.

Subfields: Medical Imaging

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1. Esther Abels
2. Anil V. Parwani
3. Liron Pantanowitz
4. F. Aeffner
5. M. Zarella
6. Jeroen Vd Laak
7. M. Bui
8. Venkata. N. P. Vemuri

Citations in the corpus

• Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association (2019, background): “Active learning is considered semi-supervised learning, which may reduce the size of required training data [49].”