Searched for: person:choprs02
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Accelerating multi-coil MR image reconstruction using weak supervision
Atalık, Arda; Chopra, Sumit; Sodickson, Daniel K
Deep-learning-based MR image reconstruction in settings where large fully sampled dataset collection is infeasible requires methods that effectively use both under-sampled and fully sampled datasets. This paper evaluates a weakly supervised, multi-coil, physics-guided approach to MR image reconstruction, leveraging both dataset types, to improve both the quality and robustness of reconstruction. A physics-guided end-to-end variational network (VarNet) is pretrained in a self-supervised manner using a 4
PMID: 39382814
ISSN: 1352-8661
CID: 5730182
Predicting Risk of Alzheimer's Diseases and Related Dementias with AI Foundation Model on Electronic Health Records
Zhu, Weicheng; Tang, Huanze; Zhang, Hao; Rajamohan, Haresh Rengaraj; Huang, Shih-Lun; Ma, Xinyue; Chaudhari, Ankush; Madaan, Divyam; Almahmoud, Elaf; Chopra, Sumit; Dodson, John A; Brody, Abraham A; Masurkar, Arjun V; Razavian, Narges
Early identification of Alzheimer's disease (AD) and AD-related dementias (ADRD) has high clinical significance, both because of the potential to slow decline through initiating FDA-approved therapies and managing modifiable risk factors, and to help persons living with dementia and their families to plan before cognitive loss makes doing so challenging. However, substantial racial and ethnic disparities in early diagnosis currently lead to additional inequities in care, urging accurate and inclusive risk assessment programs. In this study, we trained an artificial intelligence foundation model to represent the electronic health records (EHR) data with a vast cohort of 1.2 million patients within a large health system. Building upon this foundation EHR model, we developed a predictive Transformer model, named TRADE, capable of identifying risks for AD/ADRD and mild cognitive impairment (MCI), by analyzing the past sequential visit records. Amongst individuals 65 and older, our model was able to generate risk predictions for various future timeframes. On the held-out validation set, our model achieved an area under the receiver operating characteristic (AUROC) of 0.772 (95% CI: 0.770, 0.773) for identifying the AD/ADRD/MCI risks in 1 year, and AUROC of 0.735 (95% CI: 0.734, 0.736) in 5 years. The positive predictive values (PPV) in 5 years among individuals with top 1% and 5% highest estimated risks were 39.2% and 27.8%, respectively. These results demonstrate significant improvements upon the current EHR-based AD/ADRD/MCI risk assessment models, paving the way for better prognosis and management of AD/ADRD/MCI at scale.
PMCID:11071573
PMID: 38712223
CID: 5662732
FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging
Tibrewala, Radhika; Dutt, Tarun; Tong, Angela; Ginocchio, Luke; Lattanzi, Riccardo; Keerthivasan, Mahesh B; Baete, Steven H; Chopra, Sumit; Lui, Yvonne W; Sodickson, Daniel K; Chandarana, Hersh; Johnson, Patricia M
Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use. In May 2023, fastMRI was expanded to include biparametric (T2- and diffusion-weighted) prostate MRI data from a clinical population. Biparametric MRI plays a vital role in the diagnosis and management of prostate cancer. Advances in imaging methods, such as reconstructing under-sampled data from accelerated acquisitions, can improve cost-effectiveness and accessibility of prostate MRI. Raw k-space data, reconstructed images and slice, volume and exam level annotations for likelihood of prostate cancer are provided in this dataset for 47468 slices corresponding to 1560 volumes from 312 patients. This dataset facilitates AI and algorithm development for prostate image reconstruction, with the ultimate goal of enhancing prostate cancer diagnosis.
PMID: 38643291
ISSN: 2052-4463
CID: 5726322
Pancreatic Cystic Lesions: Next Generation of Radiologic Assessment
Huang, Chenchan; Chopra, Sumit; Bolan, Candice W; Chandarana, Hersh; Harfouch, Nassier; Hecht, Elizabeth M; Lo, Grace C; Megibow, Alec J
Pancreatic cystic lesions are frequently identified on cross-sectional imaging. As many of these are presumed branch-duct intraductal papillary mucinous neoplasms, these lesions generate much anxiety for the patients and clinicians, often necessitating long-term follow-up imaging and even unnecessary surgical resections. However, the incidence of pancreatic cancer is overall low for patients with incidental pancreatic cystic lesions. Radiomics and deep learning are advanced tools of imaging analysis that have attracted much attention in addressing this unmet need, however, current publications on this topic show limited success and large-scale research is needed.
PMID: 37245934
ISSN: 1558-1950
CID: 5541852
Radiology Reports Improve Visual Representations Learned from Radiographs
Huang, Haoxu; Rawlekar, Samyak; Chopra, Sumit; Deniz, Cem M
Although human's ability to visually understand the structure of the World plays a crucial role in perceiving the World and making appropriate decisions, human perception does not solely rely on vision but amalgamates the information from acoustic, verbal, and visual stimuli. An active area of research has been revolving around designing an efficient framework that adapts to multiple modalities and ideally improves the performance of existing tasks. While numerous frameworks have proved effective on natural datasets like ImageNet, a limited number of studies have been carried out in the biomedical domain. In this work, we extend the available frameworks for natural data to biomedical data by leveraging the abundant, unstructured multi-modal data available as radiology images and reports. We attempt to answer the question, "For multi-modal learning, self-supervised learning and joint learning using both learning strategies, which one improves the visual representation for downstream chest radiographs classification tasks the most?". Our experiments indicated that in limited labeled data settings with 1% and 10% labeled data, the joint learning with multi-modal and self-supervised models outperforms self-supervised learning and is at par with multi-modal learning. Additionally, we found that multi-modal learning is generally more robust on out-of-distribution datasets. The code is publicly available online.
PMCID:11234265
PMID: 38988725
ISSN: 2640-3498
CID: 5732392
A No-Math Primer on the Principles of Machine Learning for Radiologists
Lee, Matthew D; Elsayed, Mohammed; Chopra, Sumit; Lui, Yvonne W
Machine learning is becoming increasingly important in both research and clinical applications in radiology due to recent technological developments, particularly in deep learning. As these technologies are translated toward clinical practice, there is a need for radiologists and radiology trainees to understand the basic principles behind them. This primer provides an accessible introduction to the vocabulary and concepts that are central to machine learning and relevant to the radiologist.
PMID: 35339253
ISSN: 1558-5034
CID: 5190662
StarSpace: Embed All The Things!
Chapter by: Wu, Ledell; Fisch, Adam; Chopra, Sumit; Adams, Keith; Weston, Antoine Bordes Jason
in: Thirty-second AAAI Conference On Artificial Intelligence / Thirtieth Innovative Applications Of Artificial Intelligence Conference / Eighth AAAI Symposium On Educational Advances In Artificial Intelligence by
pp. 5569-5577
ISBN: 978-1-57735-800-8
CID: 4800332
Computational Television Advertising
Chapter by: Balakrishnan, Suhrid; Chopra, Sumit; Applegate, David; Urbanek, Simon
in: 12TH IEEE International Conference On Data Mining (ICDM 2012) by
pp. 71-80
ISBN: 978-1-4673-4649-8
CID: 4800342
Combining Frame and Segment Level Processing via Temporal Pooling for Phonetic Classification
Chapter by: Chopra, Sumit; Haffner, Patrick; Dimitriadis, Dimitrios
in: 12TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2011 (INTERSPEECH 2011), VOLS 1-5 by
pp. 240-243
ISBN: 978-1-61839-270-1
CID: 4800312
A unified energy-based framework for unsupervised learning
Ranzato, Marc'Aurelio; Boureau, Y. Lan; LeCun, Yann; Chopra, Sumit
We introduce a view of unsupervised learning that integrates probabilistic and non-probabilistic methods for clustering, dimensionality reduction, and feature extraction in a unified framework. In this framework, an energy function associates low energies to input points that are similar to training samples, and high energies to unobserved points. Learning consists in minimizing the energies of training samples while ensuring that the energies of unobserved ones are higher. Some traditional methods construct the architecture so that only a small number of points can have low energy, while other methods explicitly "pull up" on the energies of unobserved points. In probabilistic methods the energy of unobserved points is pulled by minimizing the log partition function, an expensive, and sometimes intractable process. We explore different and more efficient methods using an energy-based approach. In particular, we show that a simple solution is to restrict the amount of information contained in codes that represent the data. We demonstrate such a method by training it on natural image patches and by applying to image denoising.
SCOPUS:84862270523
ISSN: 1533-7928
CID: 2847192