985 research outputs found
Domain-adversarial neural networks to address the appearance variability of histopathology images
Preparing and scanning histopathology slides consists of several steps, each
with a multitude of parameters. The parameters can vary between pathology labs
and within the same lab over time, resulting in significant variability of the
tissue appearance that hampers the generalization of automatic image analysis
methods. Typically, this is addressed with ad-hoc approaches such as staining
normalization that aim to reduce the appearance variability. In this paper, we
propose a systematic solution based on domain-adversarial neural networks. We
hypothesize that removing the domain information from the model representation
leads to better generalization. We tested our hypothesis for the problem of
mitosis detection in breast cancer histopathology images and made a comparative
analysis with two other approaches. We show that combining color augmentation
with domain-adversarial training is a better alternative than standard
approaches to improve the generalization of deep learning methods.Comment: MICCAI 2017 Workshop on Deep Learning in Medical Image Analysi
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 figure
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
SFCN-OPI: Detection and Fine-grained Classification of Nuclei Using Sibling FCN with Objectness Prior Interaction
Cell nuclei detection and fine-grained classification have been fundamental
yet challenging problems in histopathology image analysis. Due to the nuclei
tiny size, significant inter-/intra-class variances, as well as the inferior
image quality, previous automated methods would easily suffer from limited
accuracy and robustness. In the meanwhile, existing approaches usually deal
with these two tasks independently, which would neglect the close relatedness
of them. In this paper, we present a novel method of sibling fully
convolutional network with prior objectness interaction (called SFCN-OPI) to
tackle the two tasks simultaneously and interactively using a unified
end-to-end framework. Specifically, the sibling FCN branches share features in
earlier layers while holding respective higher layers for specific tasks. More
importantly, the detection branch outputs the objectness prior which
dynamically interacts with the fine-grained classification sibling branch
during the training and testing processes. With this mechanism, the
fine-grained classification successfully focuses on regions with high
confidence of nuclei existence and outputs the conditional probability, which
in turn benefits the detection through back propagation. Extensive experiments
on colon cancer histology images have validated the effectiveness of our
proposed SFCN-OPI and our method has outperformed the state-of-the-art methods
by a large margin.Comment: Accepted at AAAI 201
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures
Counting of mitotic figures is a fundamental step in grading and
prognostication of several cancers. However, manual mitosis counting is tedious
and time-consuming. In addition, variation in the appearance of mitotic figures
causes a high degree of discordance among pathologists. With advances in deep
learning models, several automatic mitosis detection algorithms have been
proposed but they are sensitive to {\em domain shift} often seen in histology
images. We propose a robust and efficient two-stage mitosis detection
framework, which comprises mitosis candidate segmentation ({\em Detecting
Fast}) and candidate refinement ({\em Detecting Slow}) stages. The proposed
candidate segmentation model, termed \textit{EUNet}, is fast and accurate due
to its architectural design. EUNet can precisely segment candidates at a lower
resolution to considerably speed up candidate detection. Candidates are then
refined using a deeper classifier network, EfficientNet-B7, in the second
stage. We make sure both stages are robust against domain shift by
incorporating domain generalization methods. We demonstrate state-of-the-art
performance and generalizability of the proposed model on the three largest
publicly available mitosis datasets, winning the two mitosis domain
generalization challenge contests (MIDOG21 and MIDOG22). Finally, we showcase
the utility of the proposed algorithm by processing the TCGA breast cancer
cohort (1,125 whole-slide images) to generate and release a repository of more
than 620K mitotic figures.Comment: Extended version of the work done for MIDOG challenge submissio
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