220 research outputs found
Reversed Active Learning based Atrous DenseNet for Pathological Image Classification
Witnessed the development of deep learning in recent years, increasing number
of researches try to adopt deep learning model for medical image analysis.
However, the usage of deep learning networks for the pathological image
analysis encounters several challenges, e.g. high resolution (gigapixel) of
pathological images and lack of annotations of cancer areas. To address the
challenges, we proposed a complete framework for the pathological image
classification, which consists of a novel training strategy, namely reversed
active learning (RAL), and an advanced network, namely atrous DenseNet (ADN).
The proposed RAL can remove the mislabel patches in the training set. The
refined training set can then be used to train widely used deep learning
networks, e.g. VGG-16, ResNets, etc. A novel deep learning network, i.e. atrous
DenseNet (ADN), is also proposed for the classification of pathological images.
The proposed ADN achieves multi-scale feature extraction by integrating the
atrous convolutions to the Dense Block. The proposed RAL and ADN have been
evaluated on two pathological datasets, i.e. BACH and CCG. The experimental
results demonstrate the excellent performance of the proposed ADN + RAL
framework, i.e. the average patch-level ACAs of 94.10% and 92.05% on BACH and
CCG validation sets were achieved
Fast ScanNet : fast and dense analysis of multi-gigapixel whole-slide images for cancer metastasis detection
Lymph node metastasis is one of the most important indicators in breast cancer diagnosis, that is traditionally observed under the microscope by pathologists. In recent years, with the dramatic advance of high-throughput scanning and deep learning technology, automatic analysis of histology from whole- slide images has received a wealth of interest in the field of medical image computing, which aims to alleviate pathologists’ workload and simultaneously reduce misdiagnosis rate. However, automatic detection of lymph node metastases from whole-slide images remains a key challenge because such images are typically very large, where they can often be multiple gigabytes in size. Also, the presence of hard mimics may result in a large number of false positives. In this paper, we propose a novel method with anchor layers for model conversion, which not only leverages the efficiency of fully convolutional architectures to meet the speed requirement in clinical practice, but also densely scans the whole- slide image to achieve accurate predictions on both micro- and macro-metastases. Incorporating the strategies of asynchronous sample prefetching and hard negative mining, the network can be effectively trained. The efficacy of our method are corroborated on the benchmark dataset of 2016 Camelyon Grand Challenge. Our method achieved significant improvements in comparison with the state-of-the-art methods on tumour localization accuracy with a much faster speed and even surpassed human performance on both challenge tasks
Methods for Segmentation and Classification of Digital Microscopy Tissue Images
High-resolution microscopy images of tissue specimens provide detailed
information about the morphology of normal and diseased tissue. Image analysis
of tissue morphology can help cancer researchers develop a better understanding
of cancer biology. Segmentation of nuclei and classification of tissue images
are two common tasks in tissue image analysis. Development of accurate and
efficient algorithms for these tasks is a challenging problem because of the
complexity of tissue morphology and tumor heterogeneity. In this paper we
present two computer algorithms; one designed for segmentation of nuclei and
the other for classification of whole slide tissue images. The segmentation
algorithm implements a multiscale deep residual aggregation network to
accurately segment nuclear material and then separate clumped nuclei into
individual nuclei. The classification algorithm initially carries out
patch-level classification via a deep learning method, then patch-level
statistical and morphological features are used as input to a random forest
regression model for whole slide image classification. The segmentation and
classification algorithms were evaluated in the MICCAI 2017 Digital Pathology
challenge. The segmentation algorithm achieved an accuracy score of 0.78. The
classification algorithm achieved an accuracy score of 0.81
Rotation Equivariant CNNs for Digital Pathology
We propose a new model for digital pathology segmentation, based on the
observation that histopathology images are inherently symmetric under rotation
and reflection. Utilizing recent findings on rotation equivariant CNNs, the
proposed model leverages these symmetries in a principled manner. We present a
visual analysis showing improved stability on predictions, and demonstrate that
exploiting rotation equivariance significantly improves tumor detection
performance on a challenging lymph node metastases dataset. We further present
a novel derived dataset to enable principled comparison of machine learning
models, in combination with an initial benchmark. Through this dataset, the
task of histopathology diagnosis becomes accessible as a challenging benchmark
for fundamental machine learning research.Comment: To be presented at MICCAI 2018. Implementations of equivariant layers
available at https://github.com/basveeling/keras_gcnn . PCam details and data
at https://github.com/basveeling/pca
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
Histology images are inherently symmetric under rotation, where each
orientation is equally as likely to appear. However, this rotational symmetry
is not widely utilised as prior knowledge in modern Convolutional Neural
Networks (CNNs), resulting in data hungry models that learn independent
features at each orientation. Allowing CNNs to be rotation-equivariant removes
the necessity to learn this set of transformations from the data and instead
frees up model capacity, allowing more discriminative features to be learned.
This reduction in the number of required parameters also reduces the risk of
overfitting. In this paper, we propose Dense Steerable Filter CNNs (DSF-CNNs)
that use group convolutions with multiple rotated copies of each filter in a
densely connected framework. Each filter is defined as a linear combination of
steerable basis filters, enabling exact rotation and decreasing the number of
trainable parameters compared to standard filters. We also provide the first
in-depth comparison of different rotation-equivariant CNNs for histology image
analysis and demonstrate the advantage of encoding rotational symmetry into
modern architectures. We show that DSF-CNNs achieve state-of-the-art
performance, with significantly fewer parameters, when applied to three
different tasks in the area of computational pathology: breast tumour
classification, colon gland segmentation and multi-tissue nuclear segmentation
BACH: Grand Challenge on Breast Cancer Histology Images
Breast cancer is the most common invasive cancer in women, affecting more
than 10% of women worldwide. Microscopic analysis of a biopsy remains one of
the most important methods to diagnose the type of breast cancer. This requires
specialized analysis by pathologists, in a task that i) is highly time- and
cost-consuming and ii) often leads to nonconsensual results. The relevance and
potential of automatic classification algorithms using hematoxylin-eosin
stained histopathological images has already been demonstrated, but the
reported results are still sub-optimal for clinical use. With the goal of
advancing the state-of-the-art in automatic classification, the Grand Challenge
on BreAst Cancer Histology images (BACH) was organized in conjunction with the
15th International Conference on Image Analysis and Recognition (ICIAR 2018). A
large annotated dataset, composed of both microscopy and whole-slide images,
was specifically compiled and made publicly available for the BACH challenge.
Following a positive response from the scientific community, a total of 64
submissions, out of 677 registrations, effectively entered the competition.
From the submitted algorithms it was possible to push forward the
state-of-the-art in terms of accuracy (87%) in automatic classification of
breast cancer with histopathological images. Convolutional neuronal networks
were the most successful methodology in the BACH challenge. Detailed analysis
of the collective results allowed the identification of remaining challenges in
the field and recommendations for future developments. The BACH dataset remains
publically available as to promote further improvements to the field of
automatic classification in digital pathology.Comment: Accepted for publication at Medical Image Analysis (Elsevier).
Publication licensed under the Creative Commons CC-BY-NC-ND 4.0 license
http://creativecommons.org/licenses/by-nc-nd/4.0
A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains
Recently proposed methods for weakly-supervised semantic segmentation have
achieved impressive performance in predicting pixel classes despite being
trained with only image labels which lack positional information. Because image
annotations are cheaper and quicker to generate, weak supervision is more
practical than full supervision for training segmentation algorithms. These
methods have been predominantly developed to solve the background separation
and partial segmentation problems presented by natural scene images and it is
unclear whether they can be simply transferred to other domains with different
characteristics, such as histopathology and satellite images, and still perform
well. This paper evaluates state-of-the-art weakly-supervised semantic
segmentation methods on natural scene, histopathology, and satellite image
datasets and analyzes how to determine which method is most suitable for a
given dataset. Our experiments indicate that histopathology and satellite
images present a different set of problems for weakly-supervised semantic
segmentation than natural scene images, such as ambiguous boundaries and class
co-occurrence. Methods perform well for datasets they were developed on, but
tend to perform poorly on other datasets. We present some practical techniques
for these methods on unseen datasets and argue that more work is needed for a
generalizable approach to weakly-supervised semantic segmentation. Our full
code implementation is available on GitHub:
https://github.com/lyndonchan/wsss-analysis.Comment: 23 pages; submitted to International Journal of Computer Vision
(IJCV). Associated code available at
https://github.com/lyndonchan/wsss-analysis. To view Supplementary Materials,
please download pdf file listed under "Ancillary files
Dense steerable filter CNNs for exploiting rotational symmetry in histology images
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear. However, this rotational symmetry is not widely utilised as prior knowledge in modern Convolutional Neural Networks (CNNs), resulting in data hungry models that learn independent features at each orientation. Allowing CNNs to be rotation-equivariant removes the necessity to learn this set of transformations from the data and instead frees up model capacity, allowing more discriminative features to be learned. This reduction in the number of required parameters also reduces the risk of overfitting. In this paper, we propose Dense Steerable Filter CNNs (DSF-CNNs) that use group convolutions with multiple rotated copies of each filter in a densely connected framework. Each filter is defined as a linear combination of steerable basis filters, enabling exact rotation and decreasing the number of trainable parameters compared to standard filters. We also provide the first in-depth comparison of different rotation-equivariant CNNs for histology image analysis and demonstrate the advantage of encoding rotational symmetry into modern architectures. We show that DSF-CNNs achieve state-of-the-art performance, with significantly fewer parameters, when applied to three different tasks in the area of computational pathology: breast tumour classification, colon gland segmentation and multi-tissue nuclear segmentation
Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images
Digital histology images are amenable to the application of convolutional
neural network (CNN) for analysis due to the sheer size of pixel data present
in them. CNNs are generally used for representation learning from small image
patches (e.g. 224x224) extracted from digital histology images due to
computational and memory constraints. However, this approach does not
incorporate high-resolution contextual information in histology images. We
propose a novel way to incorporate larger context by a context-aware neural
network based on images with a dimension of 1,792x1,792 pixels. The proposed
framework first encodes the local representation of a histology image into high
dimensional features then aggregates the features by considering their spatial
organization to make a final prediction. The proposed method is evaluated for
colorectal cancer grading and breast cancer classification. A comprehensive
analysis of some variants of the proposed method is presented. Our method
outperformed the traditional patch-based approaches, problem-specific methods,
and existing context-based methods quantitatively by a margin of 3.61%. Code
and dataset related information is available at this link:
https://tia-lab.github.io/Context-Aware-CNNComment: 10 pages, 4 figures, Supplementary Documen
A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks
Breast cancer is one of the most common and deadliest cancers among women.
Since histopathological images contain sufficient phenotypic information, they
play an indispensable role in the diagnosis and treatment of breast cancers. To
improve the accuracy and objectivity of Breast Histopathological Image Analysis
(BHIA), Artificial Neural Network (ANN) approaches are widely used in the
segmentation and classification tasks of breast histopathological images. In
this review, we present a comprehensive overview of the BHIA techniques based
on ANNs. First of all, we categorize the BHIA systems into classical and deep
neural networks for in-depth investigation. Then, the relevant studies based on
BHIA systems are presented. After that, we analyze the existing models to
discover the most suitable algorithms. Finally, publicly accessible datasets,
along with their download links, are provided for the convenience of future
researchers.Comment: 25 pages,19 figure
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