261 citations · 344 across the 13 of their papers we have counts for
13 papers
Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI
Boyang Yu, Miquel Lopez Escoriza, Long Chen +3
In this work, we comprehensively evaluate three popular feature-extraction paradigms in AI-based neuroimaging modeling: (1) computation of anatomical surfaces and volumes, (2) supe…
3D Foundation Model for Generalizable Disease Detection in Head Computed Tomography
Weicheng Zhu, Haoxu Huang, Huanze Tang +10
Head computed tomography (CT) imaging is a widely-used imaging modality with multitudes of medical indications, particularly in assessing pathology of the brain, skull, and cerebro…
Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of Clinical MRI Scans
Long Chen, Liben Chen, Binfeng Xu +2
The aging population of the U.S. drives the prevalence of Alzheimer's disease. Brookmeyer et al. forecasts approximately 15 million Americans will have either clinical AD or mild c…
Making Self-supervised Learning Robust to Spurious Correlation via Learning-speed Aware Sampling
Weicheng Zhu, Sheng Liu, Carlos Fernandez-Granda +1
Self-supervised learning (SSL) has emerged as a powerful technique for learning rich representations from unlabeled data. The data representations are able to capture many underlyi…
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning
Kangning Liu, Weicheng Zhu, Yiqiu Shen +4
Learning representations for individual instances when only bag-level labels are available is a fundamental challenge in multiple instance learning (MIL). Recent works have shown p…
Interpretable Prediction of Lung Squamous Cell Carcinoma Recurrence With Self-supervised Learning
Weicheng Zhu, Carlos Fernandez-Granda, Narges Razavian
Lung squamous cell carcinoma (LSCC) has a high recurrence and metastasis rate. Factors influencing recurrence and metastasis are currently unknown and there are no distinct histopa…
Deep Probability Estimation
Sheng Liu, Aakash Kaku, Weicheng Zhu +8
Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty. Probability-estimation models are trained…
Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning
Aakash Kaku, Sahana Upadhya, Narges Razavian
We show that bringing intermediate layers' representations of two augmented versions of an image closer together in self-supervised learning helps to improve the momentum contrasti…
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
Farah E. Shamout, Yiqiu Shen, Nan Wu +17
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-…
Early-Learning Regularization Prevents Memorization of Noisy Labels
Sheng Liu, Jonathan Niles-Weed, Narges Razavian +1
We propose a novel framework to perform classification via deep learning in the presence of noisy annotations. When trained on noisy labels, deep neural networks have been observed…
Variationally Regularized Graph-based Representation Learning for Electronic Health Records
Weicheng Zhu, Narges Razavian
Electronic Health Records (EHR) are high-dimensional data with implicit connections among thousands of medical concepts. These connections, for instance, the co-occurrence of disea…
DARTS: DenseUnet-based Automatic Rapid Tool for brain Segmentation
Aakash Kaku, Chaitra V. Hegde, Jeffrey Huang +6
Quantitative, volumetric analysis of Magnetic Resonance Imaging (MRI) is a fundamental way researchers study the brain in a host of neurological conditions including normal maturat…
On the design of convolutional neural networks for automatic detection of Alzheimer's disease
Sheng Liu, Chhavi Yadav, Carlos Fernandez-Granda +1
Early detection is a crucial goal in the study of Alzheimer's Disease (AD). In this work, we describe several techniques to boost the performance of 3D deep convolutional neural ne…