Lymphocyte Classification in Hyperspectral Images of Ovarian Cancer Tissue Biopsy Samples
This work addresses the need for more efficient and accurate cancer diagnosis by automating lymphocyte classification in hyperspectral images, but it appears incremental as it applies existing machine learning methods to a specific medical domain.
The researchers tackled the problem of segmenting lymphocyte pixels in hyperspectral images of ovarian cancer biopsy samples to aid diagnosis, achieving results with methods like SVM and CNN, though no concrete numbers were provided.
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).