8 research outputs found
Automated segmentation of intracranial hemorrhages from 3D CT
Intracranial hemorrhage segmentation challenge (INSTANCE 2022) offers a
platform for researchers to compare their solutions to segmentation of
hemorrhage stroke regions from 3D CTs. In this work, we describe our solution
to INSTANCE 2022. We use a 2D segmentation network, SegResNet from MONAI,
operating slice-wise without resampling. The final submission is an ensemble of
18 models. Our solution (team name NVAUTO) achieves the top place in terms of
Dice metric (0.721), and overall rank 2. It is implemented with Auto3DSeg.Comment: INSTANCE22 challenge report, MICCAI2022. arXiv admin note:
substantial text overlap with arXiv:2209.0954
A Generalized Framework for Critical Heat Flux Detection Using Unsupervised Image-to-Image Translation
This work proposes a framework developed to generalize Critical Heat Flux
(CHF) detection classification models using an Unsupervised Image-to-Image
(UI2I) translation model. The framework enables a typical classification model
that was trained and tested on boiling images from domain A to predict boiling
images coming from domain B that was never seen by the classification model.
This is done by using the UI2I model to transform the domain B images to look
like domain A images that the classification model is familiar with. Although
CNN was used as the classification model and Fixed-Point GAN (FP-GAN) was used
as the UI2I model, the framework is model agnostic. Meaning, that the framework
can generalize any image classification model type, making it applicable to a
variety of similar applications and not limited to the boiling crisis detection
problem. It also means that the more the UI2I models advance, the better the
performance of the framework.Comment: This work has been submitted to the Expert Systems With Applications
Journal on Sep 25, 202
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR Images
Harnessing the power of deep neural networks in the medical imaging domain is
challenging due to the difficulties in acquiring large annotated datasets,
especially for rare diseases, which involve high costs, time, and effort for
annotation. Unsupervised disease detection methods, such as anomaly detection,
can significantly reduce human effort in these scenarios. While anomaly
detection typically focuses on learning from images of healthy subjects only,
real-world situations often present unannotated datasets with a mixture of
healthy and diseased subjects. Recent studies have demonstrated that utilizing
such unannotated images can improve unsupervised disease and anomaly detection.
However, these methods do not utilize knowledge specific to registered
neuroimages, resulting in a subpar performance in neurologic disease detection.
To address this limitation, we propose Brainomaly, a GAN-based image-to-image
translation method specifically designed for neurologic disease detection.
Brainomaly not only offers tailored image-to-image translation suitable for
neuroimages but also leverages unannotated mixed images to achieve superior
neurologic disease detection. Additionally, we address the issue of model
selection for inference without annotated samples by proposing a pseudo-AUC
metric, further enhancing Brainomaly's detection performance. Extensive
experiments and ablation studies demonstrate that Brainomaly outperforms
existing state-of-the-art unsupervised disease and anomaly detection methods by
significant margins in Alzheimer's disease detection using a publicly available
dataset and headache detection using an institutional dataset. The code is
available from https://github.com/mahfuzmohammad/Brainomaly.Comment: Accepted in WACV 202
Neuropsychiatric Symptoms and Commonly Used Biomarkers of Alzheimer\u27s Disease: A Literature Review from a Machine Learning Perspective
There is a growing interest in the application of machine learning (ML) in Alzheimer\u27s disease (AD) research. However, neuropsychiatric symptoms (NPS), frequent in subjects with AD, mild cognitive impairment (MCI), and other related dementias have not been analyzed sufficiently using ML methods. To portray the landscape and potential of ML research in AD and NPS studies, we present a comprehensive literature review of existing ML approaches and commonly studied AD biomarkers. We conducted PubMed searches with keywords related to NPS, AD biomarkers, machine learning, and cognition. We included a total of 38 articles in this review after excluding some irrelevant studies from the search results and including 6 articles based on a snowball search from the bibliography of the relevant studies. We found a limited number of studies focused on NPS with or without AD biomarkers. In contrast, multiple statistical machine learning and deep learning methods have been used to build predictive diagnostic models using commonly known AD biomarkers. These mainly included multiple imaging biomarkers, cognitive scores, and various omics biomarkers. Deep learning approaches that combine these biomarkers or multi-modality datasets typically outperform single-modality datasets. We conclude ML may be leveraged to untangle the complex relationships of NPS and AD biomarkers with cognition. This may potentially help to predict the progression of MCI or dementia and develop more targeted early intervention approaches based on NPS