no code implementations • 21 Dec 2023 • Wenhui Cui, Haleh Akrami, Ganning Zhao, Anand A. Joshi, Richard M. Leahy
To explore the generalizability of the foundation model in downstream applications, we then apply the model to an unseen TBI dataset for prediction of PTE using zero-shot learning.
no code implementations • 5 Oct 2023 • Omar Zamzam, Haleh Akrami, Mahdi Soltanolkotabi, Richard Leahy
In this paper we propose to learn a neural network-based data representation using a loss function that can be used to project the unlabeled data into two (positive and negative) clusters that can be easily identified using simple clustering techniques, effectively emulating the phenomenon observed in low-dimensional settings.
no code implementations • 14 Sep 2023 • Haleh Akrami, Omar Zamzam, Anand Joshi, Sergul Aydore, Richard Leahy
Outlier features can compromise the performance of deep learning regression problems such as style translation, image reconstruction, and deep anomaly detection, potentially leading to misleading conclusions.
no code implementations • 16 Dec 2022 • Wenhui Cui, Haleh Akrami, Anand A. Joshi, Richard M. Leahy
Transferring knowledge from a source domain with abundant training data to a target domain is effective for improving representation learning on scarce training data.
no code implementations • 4 Dec 2022 • Haleh Akrami, Hannes Gamper
The mean opinion score (MOS) is standardized for the perceptual evaluation of speech quality and is obtained by asking listeners to rate the quality of a speech sample.
no code implementations • 26 Aug 2022 • Omar Zamzam, Haleh Akrami, Richard Leahy
In our suggested method, the GAN discriminator instructs the generator only to produce samples that fall into the unlabeled data distribution, while a second classifier (observer) network monitors the GAN training to: (i) prevent the generated samples from falling into the positive distribution; and (ii) learn the features that are the key distinction between the positive and negative observations.
1 code implementation • 8 Aug 2022 • Haleh Akrami, Wenhui Cui, Anand A Joshi, Richard M. Leahy
Segmentation is one of the most important tasks in MRI medical image analysis and is often the first and the most critical step in many clinical applications.
1 code implementation • 3 Mar 2022 • Wenhui Cui, Haleh Akrami, Anand A. Joshi, Richard M. Leahy
The amount of manually labeled data is limited in medical applications, so semi-supervised learning and automatic labeling strategies can be an asset for training deep neural networks.
1 code implementation • 20 Sep 2021 • Haleh Akrami, Anand Joshi, Sergul Aydore, Richard Leahy
Here we address the problem of quantifying uncertainty in the images that are reconstructed by the VAE as the basis for principled outlier or lesion detection.
no code implementations • 13 Dec 2020 • Anand A. Joshi, Soyoung Choi, Haleh Akrami, Richard M. Leahy
While pointwise analysis methods are common in anatomical studies such as cortical thickness analysis and voxel- and tensor-based morphometry and its variants, such a method is lacking for rs-fMRI and could improve the utility of rs-fMRI for group studies.
no code implementations • 18 Oct 2020 • Haleh Akrami, Anand A. Joshi, Sergul Aydore, Richard M. Leahy
Using estimated quantiles to compute mean and variance under the Gaussian assumption, we compute reconstruction probability as a principled approach to outlier or anomaly detection.
no code implementations • 15 Jun 2020 • Haleh Akrami, Sergul Aydore, Richard M. Leahy, Anand A. Joshi
The source of outliers in training data include the data collection process itself (random noise) or a malicious attacker (data poisoning) who may target to degrade the performance of the machine learning model.
no code implementations • 2 Oct 2019 • Souvik Kundu, Saurav Prakash, Haleh Akrami, Peter A. Beerel, Keith M. Chugg
To explore the potential of this approach, we have experimented with two widely accepted datasets, CIFAR-10 and Tiny ImageNet, in sparse variants of both the ResNet18 and VGG16 architectures.
no code implementations • 23 May 2019 • Haleh Akrami, Anand A. Joshi, Jian Li, Sergul Aydore, Richard M. Leahy
Machine learning methods often need a large amount of labeled training data.