143 research outputs found
Abnormality Detection in Mammography using Deep Convolutional Neural Networks
Breast cancer is the most common cancer in women worldwide. The most common
screening technology is mammography. To reduce the cost and workload of
radiologists, we propose a computer aided detection approach for classifying
and localizing calcifications and masses in mammogram images. To improve on
conventional approaches, we apply deep convolutional neural networks (CNN) for
automatic feature learning and classifier building. In computer-aided
mammography, deep CNN classifiers cannot be trained directly on full mammogram
images because of the loss of image details from resizing at input layers.
Instead, our classifiers are trained on labelled image patches and then adapted
to work on full mammogram images for localizing the abnormalities.
State-of-the-art deep convolutional neural networks are compared on their
performance of classifying the abnormalities. Experimental results indicate
that VGGNet receives the best overall accuracy at 92.53\% in classifications.
For localizing abnormalities, ResNet is selected for computing class activation
maps because it is ready to be deployed without structural change or further
training. Our approach demonstrates that deep convolutional neural network
classifiers have remarkable localization capabilities despite no supervision on
the location of abnormalities is provided.Comment: 6 page
Bending invariant meshes and application to groupwise correspondences
We introduce a new bending invariant representation of a triangular mesh S. The bending invariant mesh X of S is a deformation of S that has the property that the geodesic distance between each pair of vertices on S is approximated well by the Euclidean distance between the corresponding vertices on X. Furthermore, X is intersection-free. The main advantage of the bending invariant mesh compared to previous approaches is that mesh-based features on X can be used to facilitate applications such as shape recognition or shape registration. We apply bending invariant meshes to find dense point-to-point correspondences between a number of deformed surfaces corresponding to different postures of the same non-rigid object in a fully automatic way. 1
Direct observation of magnon-phonon coupling in yttrium iron garnet
The magnetic insulator yttrium iron garnet (YIG) with a ferrimagnetic
transition temperature of 560 K has been widely used in microwave and
spintronic devices. Anomalous features in the spin Seeback effect (SSE)
voltages have been observed in Pt/YIG and attributed to the magnon-phonon
coupling. Here we use inelastic neutron scattering to map out low-energy spin
waves and acoustic phonons of YIG at 100 K as a function of increasing magnetic
field. By comparing the zero and 9.1 T data, we find that instead of splitting
and opening up gaps at the spin wave and acoustic phonon dispersion
intersecting points, magnon-phonon coupling in YIG enhances the hybridized
scattering intensity. These results are different from expectations of
conventional spin-lattice coupling, calling for new paradigms to understand the
scattering process of magnon-phonon interactions and the resulting
magnon-polarons.Comment: 5 pages, 4 figures, PRB in pres
COVID-19 Detection from Chest X-Ray Images Using Deep Convolutional Neural Networks with Weights Imprinting Approach
COVID-19 pandemic has drastically changed our lives. Chest radiographyhas been used to detect COVID-19. However, the numberof publicly available COVID-19 x-ray images is extremely limited,resulting in a highly imbalanced dataset. This is a challenge whenusing deep learning for classification and detection. In this work, wepropose the use of pre-trained deep Convolutional Neural Networks(CNN) and integrate them with a few-shot learning approach namedimprinted weights. The integrated model is fine tuned to enhancethe capability of detecting COVID-19. The proposed solution thencombines the fine-tuned models using a weighted average ensemblefor achieving an optimal 82% sensitivity to COVID-19. To thebest of authors’ knowledge, the proposed solution is one of the firstto utilize imprinted weights model with weighted average ensemblefor enhancing the model sensitivity to COVID-19
COVIDx-US -- An open-access benchmark dataset of ultrasound imaging data for AI-driven COVID-19 analytics
The COVID-19 pandemic continues to have a devastating effect on the health
and well-being of the global population. Apart from the global health crises,
the pandemic has also caused significant economic and financial difficulties
and socio-physiological implications. Effective screening, triage, treatment
planning, and prognostication of outcome plays a key role in controlling the
pandemic. Recent studies have highlighted the role of point-of-care ultrasound
imaging for COVID-19 screening and prognosis, particularly given that it is
non-invasive, globally available, and easy-to-sanitize. Motivated by these
attributes and the promise of artificial intelligence tools to aid clinicians,
we introduce COVIDx-US, an open-access benchmark dataset of COVID-19 related
ultrasound imaging data that is the largest of its kind. The COVIDx-US dataset
was curated from multiple sources and consists of 93 lung ultrasound videos and
10,774 processed images of patients infected with SARS-CoV-2 pneumonia,
non-SARS-CoV-2 pneumonia, as well as healthy control cases. The dataset was
systematically processed and validated specifically for the purpose of building
and evaluating artificial intelligence algorithms and models.Comment: 12 pages, 5 figures, to be submitted to Nature Scientific Dat
Towards Trustworthy Healthcare AI: Attention-Based Feature Learning for COVID-19 Screening With Chest Radiography
Building AI models with trustworthiness is important especially in regulated
areas such as healthcare. In tackling COVID-19, previous work uses
convolutional neural networks as the backbone architecture, which has shown to
be prone to over-caution and overconfidence in making decisions, rendering them
less trustworthy -- a crucial flaw in the context of medical imaging. In this
study, we propose a feature learning approach using Vision Transformers, which
use an attention-based mechanism, and examine the representation learning
capability of Transformers as a new backbone architecture for medical imaging.
Through the task of classifying COVID-19 chest radiographs, we investigate into
whether generalization capabilities benefit solely from Vision Transformers'
architectural advances. Quantitative and qualitative evaluations are conducted
on the trustworthiness of the models, through the use of "trust score"
computation and a visual explainability technique. We conclude that the
attention-based feature learning approach is promising in building trustworthy
deep learning models for healthcare.Comment: Accepted to 39th International Conference on Machine Learning,
Workshop on Healthcare AI and COVID-1
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