  Histologic examination plays a crucial role in oncology research and
diagnostics. The adoption of digital scanning of whole slide images (WSI) has
created an opportunity to leverage deep learning-based image classification
methods to enhance diagnosis and risk stratification. Technical limitations of
current approaches to training deep convolutional neural networks (DCNN) result
in suboptimal model performance and make training and deployment of
comprehensive classification models unobtainable. In this study, we introduce a
novel approach that addresses the main limitations of traditional
histopathology classification model training. Our method, termed Learned
Resizing with Efficient Training (LRET), couples efficient training techniques
with image resizing to facilitate seamless integration of larger histology
image patches into state-of-the-art classification models while preserving
important structural information.
  We used the LRET method coupled with two distinct resizing techniques to
train three diverse histology image datasets using multiple diverse DCNN
architectures. Our findings demonstrate a significant enhancement in
classification performance and training efficiency. Across the spectrum of
experiments, LRET consistently outperforms existing methods, yielding a
substantial improvement of 15-28% in accuracy for a large-scale, multiclass
tumor classification task consisting of 74 distinct brain tumor types. LRET not
only elevates classification accuracy but also substantially reduces training
times, unlocking the potential for faster model development and iteration. The
implications of this work extend to broader applications within medical imaging
and beyond, where efficient integration of high-resolution images into deep
learning pipelines is paramount for driving advancements in research and
clinical practice.
