Abstract
BackgroundHistopathology images of tumor biopsies present unique challenges for applying machine learning to the diagnosis and treatment of cancer. The pathology slides are high resolution, often exceeding 1GB, have non-uniform dimensions, and often contain multiple tissue slices of varying sizes surrounded by large empty regions. The locations of abnormal or cancerous cells, which may constitute a small portion of any given tissue sample, are not annotated. Cancer image datasets are also extremely imbalanced, with most slides being associated with relatively common cancers. Since deep representations trained on natural photographs are unlikely to be optimal for classifying pathology slide images, which have different spectral ranges and spatial structure, we here describe an approach for learning features and inferring representations of cancer pathology slides based on sparse coding.ResultsWe show that conventional transfer learning using a state-of-the-art deep learning architecture pre-trained on ImageNet (RESNET) and fine tuned for a binary tumor/no-tumor classification task achieved between 85% and 86% accuracy. However, when all layers up to the last convolutional layer in RESNET are replaced with a single feature map inferred via a sparse coding using a dictionary optimized for sparse reconstruction of unlabeled pathology slides, classification performance improves to over 93%, corresponding to a 54% error reduction.ConclusionsWe conclude that a feature dictionary optimized for biomedical imagery may in general support better classification performance than does conventional transfer learning using a dictionary pre-trained on natural images.
Highlights
Images of tumor biopsies have a long history in oncology, and remain an important component of cancer diagnosis and treatment; they provide promising opportunities for the application of machine learning to human health
Fischer et al BMC Bioinformatics 2018, 19(Suppl 18):489 using features trained from conventional photographic databases, i.e., “transfer learning,” it remains unclear whether such features are truly optimal for the specialized task of tumor discrimination from cancer pathology slides, for which the low-level image statistics are likely to be very different
Histological examination of tumor biopsies is a task currently performed by highly trained human pathologists, who assess the type and grade of tumors based on the appearance of thin tissue slices, typically stained with eosin and hematoxylin, in an optical microscope
Summary
Images of tumor biopsies have a long history in oncology, and remain an important component of cancer diagnosis and treatment; they provide promising opportunities for the application of machine learning to human health. Automated feature discovery has become increasingly common, and some have argued that “general purpose” image feature dictionaries (trained on ImageNet, for instance) may achieve high performance on specialized classification tasks [5,6,7]. Conventional deep learning approaches are problematical here due to the large, non-uniform image sizes, limited amount of training examples and imbalanced nature of the image data, and the sometime necessity for labeling (e.g. annotations that distinguish normal from cancerous tissue within an image); much of the substantial body of work in this area has been focused on segmentation within an image [10] or limited to a small number of tumor types [7, 11,12,13,14]. Since deep representations trained on natural photographs are unlikely to be optimal for classifying pathology slide images, which have different spectral ranges and spatial structure, we here describe an approach for learning features and inferring representations of cancer pathology slides based on sparse coding
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