Search Results for author:
Found 13 papers, 5 papers with code
Ordinal Classification with Distance Regularization for Robust Brain Age Prediction
However, these methods are subject to an inherent regression to the mean effect, which causes a systematic bias resulting in an overestimation of brain age in young subjects and underestimation in old subjects.
Ranked #1 on
Ordinal Classification
on OASIS+NACC+ICBM+ABIDE+IXI
Domain-knowledge Inspired Pseudo Supervision (DIPS) for Unsupervised Image-to-Image Translation Models to Support Cross-Domain Classification
Cross-domain classification frameworks were developed to handle this data domain shift problem by utilizing unsupervised image-to-image translation models to translate an input image from the unlabeled domain to the labeled domain.
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.
Ranked #1 on
Anomaly Detection
on ADNI
A Framework for Generalizing Critical Heat Flux Detection Models Using Unsupervised Image-to-Image Translation
To deal with datasets from new domains a model needs to be trained from scratch.
HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease
Therefore, this paper poses the research question of how to improve unsupervised anomaly detection by utilizing (1) an unannotated set of mixed images, in addition to (2) the set of healthy images as being used in the literature.
Deep residual inception encoder-decoder network for amyloid PET harmonization
Multiple positron emission tomography (PET) tracers are available for amyloid imaging, posing a significant challenge to consensus interpretation and quantitative analysis.
Beyond Point Clouds: A Knowledge-Aided High Resolution Imaging Radar Deep Detector for Autonomous Driving
We present a multi-input multi-output (MIMO) radar transmit and receive signal processing chain, a knowledge-aided approach exploiting the radar domain knowledge and signal structure, to generate high resolution radar range-azimuth spectra for object detection and classification using deep neural networks.
A2B-GAN: Utilizing Unannotated Anomalous Images for Anomaly Detection in Medical Image Analysis
This paper poses the research question of how to improve anomaly detection by using an unannotated set of mixed images of both normal and anomalous samples (in addition to a set of normal images from healthy subjects).
A Preliminary Comparison Between Compressive Sampling and Anisotropic Mesh-based Image Representation
Compressed sensing (CS) has become a popular field in the last two decades to represent and reconstruct a sparse signal with much fewer samples than the signal itself.
SD-CNN: a Shallow-Deep CNN for Improved Breast Cancer Diagnosis
To evaluate the validity of our approach, we first develop a deep-CNN using 49 CEDM cases collected from Mayo Clinic to prove the contributions from recombined images for improved breast cancer diagnosis (0. 86 in accuracy using LE imaging vs. 0. 90 in accuracy using both LE and recombined imaging).
Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis
This is especially true for early AD, at which stage the disease-related regions are most likely to be weak-effect regions that are difficult to be detected from a single modality alone.
Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data
Recent advances in neuroimaging techniques provide great potentials for effective diagnosis of Alzheimer’s disease (AD), the most common form of dementia.
