103,078 research outputs found
Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images
Histopathological characterization of colorectal polyps is an important
principle for determining the risk of colorectal cancer and future rates of
surveillance for patients. This characterization is time-intensive, requires
years of specialized training, and suffers from significant inter-observer and
intra-observer variability. In this work, we built an automatic
image-understanding method that can accurately classify different types of
colorectal polyps in whole-slide histology images to help pathologists with
histopathological characterization and diagnosis of colorectal polyps. The
proposed image-understanding method is based on deep-learning techniques, which
rely on numerous levels of abstraction for data representation and have shown
state-of-the-art results for various image analysis tasks. Our
image-understanding method covers all five polyp types (hyperplastic polyp,
sessile serrated polyp, traditional serrated adenoma, tubular adenoma, and
tubulovillous/villous adenoma) that are included in the US multi-society task
force guidelines for colorectal cancer risk assessment and surveillance, and
encompasses the most common occurrences of colorectal polyps. Our evaluation on
239 independent test samples shows our proposed method can identify the types
of colorectal polyps in whole-slide images with a high efficacy (accuracy:
93.0%, precision: 89.7%, recall: 88.3%, F1 score: 88.8%). The presented method
in this paper can reduce the cognitive burden on pathologists and improve their
accuracy and efficiency in histopathological characterization of colorectal
polyps, and in subsequent risk assessment and follow-up recommendations
Assessment of algorithms for mitosis detection in breast cancer histopathology images
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues.
In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists
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