84 research outputs found
Augmented Mitotic Cell Count using Field Of Interest Proposal
Histopathological prognostication of neoplasia including most tumor grading
systems are based upon a number of criteria. Probably the most important is the
number of mitotic figures which are most commonly determined as the mitotic
count (MC), i.e. number of mitotic figures within 10 consecutive high power
fields. Often the area with the highest mitotic activity is to be selected for
the MC. However, since mitotic activity is not known in advance, an arbitrary
choice of this region is considered one important cause for high variability in
the prognostication and grading.
In this work, we present an algorithmic approach that first calculates a
mitotic cell map based upon a deep convolutional network. This map is in a
second step used to construct a mitotic activity estimate. Lastly, we select
the image segment representing the size of ten high power fields with the
overall highest mitotic activity as a region proposal for an expert MC
determination. We evaluate the approach using a dataset of 32 completely
annotated whole slide images, where 22 were used for training of the network
and 10 for test. We find a correlation of r=0.936 in mitotic count estimate.Comment: 6 pages, submitted to BVM 2019 (bvm-workshop.org
Domain Generalization in Computational Pathology: Survey and Guidelines
Deep learning models have exhibited exceptional effectiveness in
Computational Pathology (CPath) by tackling intricate tasks across an array of
histology image analysis applications. Nevertheless, the presence of
out-of-distribution data (stemming from a multitude of sources such as
disparate imaging devices and diverse tissue preparation methods) can cause
\emph{domain shift} (DS). DS decreases the generalization of trained models to
unseen datasets with slightly different data distributions, prompting the need
for innovative \emph{domain generalization} (DG) solutions. Recognizing the
potential of DG methods to significantly influence diagnostic and prognostic
models in cancer studies and clinical practice, we present this survey along
with guidelines on achieving DG in CPath. We rigorously define various DS
types, systematically review and categorize existing DG approaches and
resources in CPath, and provide insights into their advantages, limitations,
and applicability. We also conduct thorough benchmarking experiments with 28
cutting-edge DG algorithms to address a complex DG problem. Our findings
suggest that careful experiment design and CPath-specific Stain Augmentation
technique can be very effective. However, there is no one-size-fits-all
solution for DG in CPath. Therefore, we establish clear guidelines for
detecting and managing DS depending on different scenarios. While most of the
concepts, guidelines, and recommendations are given for applications in CPath,
we believe that they are applicable to most medical image analysis tasks as
well.Comment: Extended Versio
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
Mitosis Detection Under Limited Annotation: A Joint Learning Approach
Mitotic counting is a vital prognostic marker of tumor proliferation in
breast cancer. Deep learning-based mitotic detection is on par with
pathologists, but it requires large labeled data for training. We propose a
deep classification framework for enhancing mitosis detection by leveraging
class label information, via softmax loss, and spatial distribution information
among samples, via distance metric learning. We also investigate strategies
towards steadily providing informative samples to boost the learning. The
efficacy of the proposed framework is established through evaluation on ICPR
2012 and AMIDA 2013 mitotic data. Our framework significantly improves the
detection with small training data and achieves on par or superior performance
compared to state-of-the-art methods for using the entire training data.Comment: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI
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
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all
malignancies since 2020. Breast imaging plays a significant role in early
diagnosis and intervention to improve the outcome of breast cancer patients. In
the past decade, deep learning has shown remarkable progress in breast cancer
imaging analysis, holding great promise in interpreting the rich information
and complex context of breast imaging modalities. Considering the rapid
improvement in the deep learning technology and the increasing severity of
breast cancer, it is critical to summarize past progress and identify future
challenges to be addressed. In this paper, we provide an extensive survey of
deep learning-based breast cancer imaging research, covering studies on
mammogram, ultrasound, magnetic resonance imaging, and digital pathology images
over the past decade. The major deep learning methods, publicly available
datasets, and applications on imaging-based screening, diagnosis, treatment
response prediction, and prognosis are described in detail. Drawn from the
findings of this survey, we present a comprehensive discussion of the
challenges and potential avenues for future research in deep learning-based
breast cancer imaging.Comment: Survey, 41 page
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