Cervical intraepithelial neoplasia (CIN) grade of histopathology images is a crucial indicator in cervical biopsy results. Accurate CIN grading of epithelium regions helps pathologists with precancerous lesion diagnosis and treatment planning. Although an automated CIN grading system has been desired, supervised training of such a system would require a large amount of expert annotations, which are expensive and time-consuming to collect. In this paper, we investigate the CIN grade classification problem on segmented epithelium patches. We propose to use conditional Generative Adversarial Networks (cGANs) to expand the limited training dataset, by synthesizing realistic cervical histopathology images. While the synthetic images are visually appealing, they are not guaranteed to contain meaningful features for data augmentation. To tackle this issue, we propose a synthetic-image filtering mechanism based on the divergence in feature space between generated images and class centroids in order to control the feature quality of selected synthetic images for data augmentation. Our models are evaluated on a cervical histopathology image dataset with limited number of patch-level CIN grade annotations. Extensive experimental results show a significant improvement of classification accuracy from 66.3% to 71.7% using the same ResNet18 baseline classifier after leveraging our cGAN generated images with feature based filtering, which demonstrates the effectiveness of our models.
The
study of Alzheimer’s disease (AD), the most common cause
of dementia, faces challenges in terms of understanding the cause,
monitoring the pathogenesis, and developing early diagnoses and effective
treatments. Rapid and accurate identification of AD biomarkers in
the brain is critical to providing key insights into AD and facilitating
the development of early diagnosis methods. In this work, we developed
a platform that enables a rapid screening of AD biomarkers by employing
graphene-assisted Raman spectroscopy and machine learning interpretation
in AD transgenic animal brains. Specifically, we collected Raman spectra
on slices of mouse brains with and without AD and used machine learning
to classify AD and non-AD spectra. By contacting monolayer graphene
with the brain slices, the accuracy was increased from 77% to 98%
in machine learning classification. Further, using a linear support
vector machine (SVM), we identified a spectral feature importance
map that reveals the importance of each Raman wavenumber in classifying
AD and non-AD spectra. Based on this spectral feature importance map,
we identified AD biomarkers including Aβ and tau proteins and
other potential biomarkers, such as triolein, phosphatidylcholine,
and actin, which have been confirmed by other biochemical studies.
Our Raman–machine learning integrated method with interpretability
will facilitate the study of AD and can be extended to other tissues
and biofluids and for various other diseases.
Significance
A large Raman dataset collected on a variety of viruses enables the training of machine learning (ML) models capable of highly accurate and sensitive virus identification. The trained ML models can then be integrated with a portable device to provide real-time virus detection and identification capability. We validate this conceptual framework by presenting highly accurate virus type and subtype identification results using a convolutional neural network to classify Raman spectra of viruses. The accurate and interpretable ML model developed for Raman virus identification presents promising potential in a real-time, label-free virus detection system that could be used in future outbreaks and pandemics.
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