Two-tier classifier for identifying small objects in histological tissue classification: experiments with colon cancer tissue mapping

Tissue classification on histological images is a useful alternative to manual histology analysis, and has been well-studied in a variety of machine learning approaches. However, classification of whole slide images at high resolution is a difficult and computationally-intensive task. In addition, many tissue analysis tasks are targeted at identifying rare or small regions of tissue. In colon cancer, small groups of tumor cells (tumor buds) exist on the front edge of the invasive tumor region and are an important indicator of cancer aggressiveness. These small objects are difficult or impossible to detect when examining an image at lower resolution, while running the classifier at an appropriate high resolution can be time consuming. In this work, a two-tier convolutional neural network classification approach is explored to identify small but important tissue regions on whole-slide tissue scans. The first tier is a coarse-level classifier trained with patches extracted from the image at a low power field (4x optical magnification), designed to identify two main tissue types: tumor and nontumor areas. Regions that are likely to contain tumor buds (non-tumor regions) are passed to a fine-level classifier that classifies the patches into 9 additional tissue types at a high-power field (40x). The system achieves a 43% reduction in processing time (3 hours to 1.7 hours for a 19,200-by-19,200 pixel image). The two-tier classifier provides an efficient whole-slide tissue classification by narrowing down the regions of interest, increasing the chances of tumor buds being identified.

[1]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[2]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Roelf J. Wieringa Case study research in information systems engineering: how to generalize, how not to generalize, and how not to generalize too much , 2013, CAISE 2013.

[4]  Olaf Hellwich,et al.  A Multi-resolution Approach for Combining Visual Information using Nuclei Segmentation and Classification in Histopathological Images , 2015, VISAPP.

[5]  Inti Zlobec,et al.  Tumor budding in colorectal cancer--ready for diagnostic practice? , 2016, Human pathology.

[6]  A. Jemal,et al.  Cancer statistics, 2019 , 2019, CA: a cancer journal for clinicians.

[7]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  R J Salmon,et al.  [Prognostic factors of colorectal cancer]. , 1989, Pathologie-biologie.

[9]  M E Hammond,et al.  Prognostic factors in colorectal cancer. College of American Pathologists Consensus Statement 1999. , 2000, Archives of pathology & laboratory medicine.

[10]  Luca Maria Gambardella,et al.  Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks , 2013, MICCAI.

[11]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Wangmeng Zuo,et al.  Learning Deep CNN Denoiser Prior for Image Restoration , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Hanlin L. Wang,et al.  Colorectal carcinoma: Pathologic aspects. , 2012, Journal of gastrointestinal oncology.

[14]  Martin Urschler,et al.  Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation , 2015, ArXiv.

[15]  H. Mitomi,et al.  Tumor Budding as an Index to Identify High-Risk Patients with Stage II Colon Cancer , 2008, Diseases of the colon and rectum.

[16]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[17]  Kaiming He,et al.  Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Francesco Bianconi,et al.  Multi-class texture analysis in colorectal cancer histology , 2016, Scientific Reports.