6 research outputs found
Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network
Histology imaging is an essential diagnosis method to finalize the grade and
stage of cancer of different tissues, especially for breast cancer diagnosis.
Specialists often disagree on the final diagnosis on biopsy tissue due to the
complex morphological variety. Although convolutional neural networks (CNN)
have advantages in extracting discriminative features in image classification,
directly training a CNN on high resolution histology images is computationally
infeasible currently. Besides, inconsistent discriminative features often
distribute over the whole histology image, which incurs challenges in
patch-based CNN classification method. In this paper, we propose a novel
architecture for automatic classification of high resolution histology images.
First, an adapted residual network is employed to explore hierarchical features
without attenuation. Second, we develop a robust deep fusion network to utilize
the spatial relationship between patches and learn to correct the prediction
bias generated from inconsistent discriminative feature distribution. The
proposed method is evaluated using 10-fold cross-validation on 400 high
resolution breast histology images with balanced labels and reports 95%
accuracy on 4-class classification and 98.5% accuracy, 99.6% AUC on 2-class
classification (carcinoma and non-carcinoma), which substantially outperforms
previous methods and close to pathologist performance.Comment: 8 pages, MICCAI workshop preceeding
CELNet: Evidence Localization for Pathology Images using Weakly Supervised Learning
Despite deep convolutional neural networks boost the performance of image
classification and segmentation in digital pathology analysis, they are usually
weak in interpretability for clinical applications or require heavy annotations
to achieve object localization. To overcome this problem, we propose a weakly
supervised learning-based approach that can effectively learn to localize the
discriminative evidence for a diagnostic label from weakly labeled training
data. Experimental results show that our proposed method can reliably pinpoint
the location of cancerous evidence supporting the decision of interest, while
still achieving a competitive performance on glimpse-level and slide-level
histopathologic cancer detection tasks.Comment: Accepted for MICCAI 201
Gigapixel Histopathological Image Analysis using Attention-based Neural Networks
Although CNNs are widely considered as the state-of-the-art models in various
applications of image analysis, one of the main challenges still open is the
training of a CNN on high resolution images. Different strategies have been
proposed involving either a rescaling of the image or an individual processing
of parts of the image. Such strategies cannot be applied to images, such as
gigapixel histopathological images, for which a high reduction in resolution
inherently effects a loss of discriminative information, and in respect of
which the analysis of single parts of the image suffers from a lack of global
information or implies a high workload in terms of annotating the training
images in such a way as to select significant parts. We propose a method for
the analysis of gigapixel histopathological images solely by using weak
image-level labels. In particular, two analysis tasks are taken into account: a
binary classification and a prediction of the tumor proliferation score. Our
method is based on a CNN structure consisting of a compressing path and a
learning path. In the compressing path, the gigapixel image is packed into a
grid-based feature map by using a residual network devoted to the feature
extraction of each patch into which the image has been divided. In the learning
path, attention modules are applied to the grid-based feature map, taking into
account spatial correlations of neighboring patch features to find regions of
interest, which are then used for the final whole slide analysis. Our method
integrates both global and local information, is flexible with regard to the
size of the input images and only requires weak image-level labels. Comparisons
with different methods of the state-of-the-art on two well known datasets,
Camelyon16 and TUPAC16, have been made to confirm the validity of the proposed
model.Comment: The manuscript was submitted to a peer-review journal on January 27t
xPath: Human-AI Diagnosis in Pathology with Multi-Criteria Analyses and Explanation by Hierarchically Traceable Evidence
Data-driven AI promises support for pathologists to discover sparse tumor
patterns in high-resolution histological images. However, from a pathologist's
point of view, existing AI suffers from three limitations: (i) a lack of
comprehensiveness where most AI algorithms only rely on a single criterion;
(ii) a lack of explainability where AI models tend to work as 'black boxes'
with little transparency; and (iii) a lack of integrability where it is unclear
how AI can become part of pathologists' existing workflow. Based on a formative
study with pathologists, we propose two designs for a human-AI collaborative
tool: (i) presenting joint analyses of multiple criteria at the top level while
(ii) revealing hierarchically traceable evidence on-demand to explain each
criterion. We instantiate such designs in xPath -- a brain tumor grading tool
where a pathologist can follow a top-down workflow to oversee AI's findings. We
conducted a technical evaluation and work sessions with twelve medical
professionals in pathology across three medical centers. We report quantitative
and qualitative feedback, discuss recurring themes on how our participants
interacted with xPath, and provide initial insights for future physician-AI
collaborative tools.Comment: 31 pages, 11 figure
3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images
Automated grading systems using deep convolution neural networks (DCNNs) have proven their capability and potential to distinguish between different breast cancer grades using digitized histopathological images. In digital breast pathology, it is vital to measure how confident a DCNN is in grading using a machine-confidence metric, especially with the presence of major computer vision challenging problems such as the high visual variability of the images. Such a quantitative metric can be employed not only to improve the robustness of automated systems, but also to assist medical professionals in identifying complex cases. In this paper, we propose Entropy-based Elastic Ensemble of DCNN models (3E-Net) for grading invasive breast carcinoma microscopy images which provides an initial stage of explainability (using an uncertainty-aware mechanism adopting entropy). Our proposed model has been designed in a way to (1) exclude images that are less sensitive and highly uncertain to our ensemble model and (2) dynamically grade the non-excluded images using the certain models in the ensemble architecture. We evaluated two variations of 3E-Net on an invasive breast carcinoma dataset and we achieved grading accuracy of 96.15% and 99.50%
A survey on artificial intelligence in histopathology image analysis
The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning-based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field