Improving Performance in Colorectal Cancer Histology Decomposition using
  Deep and Ensemble Machine Learning
Fabi Prezja Leevi Annala Sampsa Kiiskinen Suvi Lahtinen Timo Ojala Pekka Ruusuvuori Teijo Kuopio
In routine colorectal cancer management, histologic samples stained with
hematoxylin and eosin are commonly used. Nonetheless, their potential for
defining objective biomarkers for patient stratification and treatment
selection is still being explored. The current gold standard relies on
expensive and time-consuming genetic tests. However, recent research highlights
the potential of convolutional neural networks (CNNs) in facilitating the
extraction of clinically relevant biomarkers from these readily available
images. These CNN-based biomarkers can predict patient outcomes comparably to
golden standards, with the added advantages of speed, automation, and minimal
cost. The predictive potential of CNN-based biomarkers fundamentally relies on
the ability of convolutional neural networks (CNNs) to classify diverse tissue
types from whole slide microscope images accurately. Consequently, enhancing
the accuracy of tissue class decomposition is critical to amplifying the
prognostic potential of imaging-based biomarkers. This study introduces a
hybrid Deep and ensemble machine learning model that surpassed all preceding
solutions for this classification task. Our model achieved 96.74% accuracy on
the external test set and 99.89% on the internal test set. Recognizing the
potential of these models in advancing the task, we have made them publicly
available for further research and development.