9,656 research outputs found
A Compact Representation of Histopathology Images using Digital Stain Separation & Frequency-Based Encoded Local Projections
In recent years, histopathology images have been increasingly used as a
diagnostic tool in the medical field. The process of accurately diagnosing a
biopsy sample requires significant expertise in the field, and as such can be
time-consuming and is prone to uncertainty and error. With the advent of
digital pathology, using image recognition systems to highlight problem areas
or locate similar images can aid pathologists in making quick and accurate
diagnoses. In this paper, we specifically consider the encoded local
projections (ELP) algorithm, which has previously shown some success as a tool
for classification and recognition of histopathology images. We build on the
success of the ELP algorithm as a means for image classification and
recognition by proposing a modified algorithm which captures the local
frequency information of the image. The proposed algorithm estimates local
frequencies by quantifying the changes in multiple projections in local windows
of greyscale images. By doing so we remove the need to store the full
projections, thus significantly reducing the histogram size, and decreasing
computation time for image retrieval and classification tasks. Furthermore, we
investigate the effectiveness of applying our method to histopathology images
which have been digitally separated into their hematoxylin and eosin stain
components. The proposed algorithm is tested on the publicly available invasive
ductal carcinoma (IDC) data set. The histograms are used to train an SVM to
classify the data. The experiments showed that the proposed method outperforms
the original ELP algorithm in image retrieval tasks. On classification tasks,
the results are found to be comparable to state-of-the-art deep learning
methods and better than many handcrafted features from the literature.Comment: Accepted for publication in the International Conference on Image
Analysis and Recognition (ICIAR 2019
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and
correlation with overall survival. TIL map structural patterns were grouped using standard
histopathological parameters. These patterns are enriched in particular T cell subpopulations
derived from molecular measures. TIL densities and spatial structure were differentially enriched
among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial
infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic
patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for
the TCGA image archives with insights into the tumor-immune microenvironment
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