1 code implementation • 24 Apr 2024 • Yiming Che, Fazle Rafsani, Jay Shah, Md Mahfuzur Rahman Siddiquee, Teresa Wu
To tackle this challenge, we introduce Anomaly Segmentation with Forward Process of Diffusion Models (AnoFPDM), a fully weakly-supervised framework that operates without the need for pixel-level labels.
no code implementations • 14 Feb 2024 • Rajeev Goel, Utkarsh Nath, Yancheng Wang, Alvin C. Silva, Teresa Wu, Yingzhen Yang
To address this challenge, we propose a novel Low-Rank Feature Learning (LRFL) method in this paper, which is universally applicable to the training of all neural networks.
1 code implementation • IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 • Jay Shah, Md Mahfuzur Rahman Siddiquee, Yi Su, Teresa Wu, Baoxin Li
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
2 code implementations • 18 Mar 2023 • Firas Al-Hindawi, Md Mahfuzur Rahman Siddiquee, Teresa Wu, Han Hu, Ying Sun
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.
1 code implementation • 18 Feb 2023 • Md Mahfuzur Rahman Siddiquee, Jay Shah, Teresa Wu, Catherine Chong, Todd J. Schwedt, Gina Dumkrieger, Simona Nikolova, Baoxin Li
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
no code implementations • 18 Dec 2022 • Firas Al-Hindawi, Tejaswi Soori, Han Hu, Md Mahfuzur Rahman Siddiquee, Hyunsoo Yoon, Teresa Wu, Ying Sun
To deal with datasets from new domains a model needs to be trained from scratch.
1 code implementation • 5 Sep 2022 • Md Mahfuzur Rahman Siddiquee, Jay Shah, Teresa Wu, Catherine Chong, Todd Schwedt, Baoxin Li
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.
1 code implementation • Alzheimer's and Dementia 2022 • Jay Shah, Fei Gao, Baoxin Li, Valentina Ghisays, Ji Luo, Yinghua Chen, Wendy Lee, Yuxiang Zhou, Tammie L.S. Benzinger, Eric M. Reiman, Kewei Chen, Yi Su, Teresa Wu
Multiple positron emission tomography (PET) tracers are available for amyloid imaging, posing a significant challenge to consensus interpretation and quantitative analysis.
no code implementations • 1 Nov 2021 • Ruxin Zheng, Shunqiao Sun, David Scharff, Teresa Wu
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.
no code implementations • 29 Sep 2021 • Md Mahfuzur Rahman Siddiquee, Teresa Wu, Baoxin Li
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).
no code implementations • 19 Nov 2020 • Xianping Li, Teresa Wu
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.
no code implementations • 1 Mar 2018 • Fei Gao, Teresa Wu, Jing Li, Bin Zheng, Lingxiang Ruan, Desheng Shang, Bhavika Patel
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).
no code implementations • NeurIPS 2011 • Shuai Huang, Jing Li, Jieping Ye, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman
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.
no code implementations • NeurIPS 2009 • Shuai Huang, Jing Li, Liang Sun, Jun Liu, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman, Jieping Ye
Recent advances in neuroimaging techniques provide great potentials for effective diagnosis of Alzheimer’s disease (AD), the most common form of dementia.