  Current methods for diagnosing the progression of multiple types of cancer
within patients rely on interpreting stained needle biopsies. This process is
time-consuming and susceptible to error throughout the paraffinization,
Hematoxylin and Eosin (H&E) staining, deparaffinization, and annotation stages.
Fourier Transform Infrared (FTIR) imaging has been shown to be a promising
alternative to staining for appropriately annotating biopsy cores without the
need for deparaffinization or H&E staining with the use of Fourier Transform
Infrared (FTIR) images when combined with machine learning to interpret the
dense spectral information. We present a machine learning pipeline to segment
white blood cell (lymphocyte) pixels in hyperspectral images of biopsy cores.
These cells are clinically important for diagnosis, but some prior work has
struggled to incorporate them due to difficulty obtaining precise pixel labels.
Evaluated methods include Support Vector Machine (SVM), Gaussian Naive Bayes,
and Multilayer Perceptron (MLP), as well as analyzing the comparatively modern
convolutional neural network (CNN).
