25 results on '"Sarma, Karthik V"'
Search Results
2. Optimizing Spatial Biopsy Sampling for the Detection of Prostate Cancer
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Raman, Alex G, Sarma, Karthik V, Raman, Steven S, Priester, Alan M, Mirak, Sohrab Afshari, Riskin-Jones, Hannah H, Dhinagar, Nikhil, Speier, William, Felker, Ely, Sisk, Anthony E, Lu, David, Kinnaird, Adam, Reiter, Robert E, Marks, Leonard S, and Arnold, Corey W
- Subjects
Biomedical and Clinical Sciences ,Clinical Sciences ,Oncology and Carcinogenesis ,Urologic Diseases ,Prostate Cancer ,Prevention ,Biomedical Imaging ,Clinical Research ,Cancer ,Aged ,Biopsy ,Large-Core Needle ,Datasets as Topic ,Feasibility Studies ,Humans ,Image-Guided Biopsy ,Magnetic Resonance Imaging ,Interventional ,Male ,Middle Aged ,Multimodal Imaging ,Multiparametric Magnetic Resonance Imaging ,Neoplasm Grading ,Prostate ,Prostatectomy ,Prostatic Neoplasms ,Retrospective Studies ,Spatial Analysis ,Ultrasonography ,Interventional ,prostatic neoplasms ,image-guided biopsy ,biopsy ,adverse effects ,ultrasonography ,interventional ,magnetic resonance imaging - Abstract
PurposeThe appropriate number of systematic biopsy cores to retrieve during magnetic resonance imaging (MRI)-targeted prostate biopsy is not well defined. We aimed to demonstrate a biopsy sampling approach that reduces required core count while maintaining diagnostic performance.Materials and methodsWe collected data from a cohort of 971 men who underwent MRI-ultrasound fusion targeted biopsy for suspected prostate cancer. A regional targeted biopsy (RTB) was evaluated retrospectively; only cores within 2 cm of the margin of a radiologist-defined region of interest were considered part of the RTB. We compared detection rates for clinically significant prostate cancer (csPCa) and cancer upgrading rate on final whole mount pathology after prostatectomy between RTB, combined, MRI-targeted, and systematic biopsy.ResultsA total of 16,459 total cores from 971 men were included in the study data sets, of which 1,535 (9%) contained csPCa. The csPCa detection rates for systematic, MRI-targeted, combined, and RTB were 27.0% (262/971), 38.3% (372/971), 44.8% (435/971), and 44.0% (427/971), respectively. Combined biopsy detected significantly more csPCa than systematic and MRI-targeted biopsy (p
- Published
- 2021
3. Federated learning improves site performance in multicenter deep learning without data sharing
- Author
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Sarma, Karthik V, Harmon, Stephanie, Sanford, Thomas, Roth, Holger R, Xu, Ziyue, Tetreault, Jesse, Xu, Daguang, Flores, Mona G, Raman, Alex G, Kulkarni, Rushikesh, Wood, Bradford J, Choyke, Peter L, Priester, Alan M, Marks, Leonard S, Raman, Steven S, Enzmann, Dieter, Turkbey, Baris, Speier, William, and Arnold, Corey W
- Subjects
Information and Computing Sciences ,Machine Learning ,Deep Learning ,Humans ,Information Dissemination ,Privacy ,deep learning ,federated learning ,privacy ,generalizability ,prostate ,Engineering ,Medical and Health Sciences ,Medical Informatics ,Biomedical and clinical sciences ,Health sciences ,Information and computing sciences - Abstract
ObjectiveTo demonstrate enabling multi-institutional training without centralizing or sharing the underlying physical data via federated learning (FL).Materials and methodsDeep learning models were trained at each participating institution using local clinical data, and an additional model was trained using FL across all of the institutions.ResultsWe found that the FL model exhibited superior performance and generalizability to the models trained at single institutions, with an overall performance level that was significantly better than that of any of the institutional models alone when evaluated on held-out test sets from each institution and an outside challenge dataset.DiscussionThe power of FL was successfully demonstrated across 3 academic institutions while avoiding the privacy risk associated with the transfer and pooling of patient data.ConclusionFederated learning is an effective methodology that merits further study to enable accelerated development of models across institutions, enabling greater generalizability in clinical use.
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- 2021
4. Harnessing clinical annotations to improve deep learning performance in prostate segmentation
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Sarma, Karthik V, Raman, Alex G, Dhinagar, Nikhil J, Priester, Alan M, Harmon, Stephanie, Sanford, Thomas, Mehralivand, Sherif, Turkbey, Baris, Marks, Leonard S, Raman, Steven S, Speier, William, and Arnold, Corey W
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Biomedical and Clinical Sciences ,Oncology and Carcinogenesis ,Urologic Diseases ,Good Health and Well Being ,Data Curation ,Databases ,Factual ,Deep Learning ,Humans ,Male ,Prostate ,Retrospective Studies ,Tomography ,X-Ray Computed ,General Science & Technology - Abstract
PurposeDeveloping large-scale datasets with research-quality annotations is challenging due to the high cost of refining clinically generated markup into high precision annotations. We evaluated the direct use of a large dataset with only clinically generated annotations in development of high-performance segmentation models for small research-quality challenge datasets.Materials and methodsWe used a large retrospective dataset from our institution comprised of 1,620 clinically generated segmentations, and two challenge datasets (PROMISE12: 50 patients, ProstateX-2: 99 patients). We trained a 3D U-Net convolutional neural network (CNN) segmentation model using our entire dataset, and used that model as a template to train models on the challenge datasets. We also trained versions of the template model using ablated proportions of our dataset, and evaluated the relative benefit of those templates for the final models. Finally, we trained a version of the template model using an out-of-domain brain cancer dataset, and evaluated the relevant benefit of that template for the final models. We used five-fold cross-validation (CV) for all training and evaluation across our entire dataset.ResultsOur model achieves state-of-the-art performance on our large dataset (mean overall Dice 0.916, average Hausdorff distance 0.135 across CV folds). Using this model as a pre-trained template for refining on two external datasets significantly enhanced performance (30% and 49% enhancement in Dice scores respectively). Mean overall Dice and mean average Hausdorff distance were 0.912 and 0.15 for the ProstateX-2 dataset, and 0.852 and 0.581 for the PROMISE12 dataset. Using even small quantities of data to train the template enhanced performance, with significant improvements using 5% or more of the data.ConclusionWe trained a state-of-the-art model using unrefined clinical prostate annotations and found that its use as a template model significantly improved performance in other prostate segmentation tasks, even when trained with only 5% of the original dataset.
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- 2021
5. Semi-automated PIRADS scoring via mpMRI analysis
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Dhinagar, Nikhil J, Speier, William, Sarma, Karthik V, Raman, Alex, Kinnaird, Adam, Raman, Steven S, Marks, Leonard S, and Arnold, Corey W
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Biomedical and Clinical Sciences ,Clinical Sciences ,Oncology and Carcinogenesis ,Prostate Cancer ,Urologic Diseases ,Aging ,Cancer ,medical image analysis ,prostate cancer ,multiparametric magnetic resonance imaging ,Prostate Imaging-Reporting and Data System ,deep learning ,Clinical sciences ,Biomedical engineering - Abstract
Purpose: Prostate cancer (PCa) is the most common solid organ cancer and second leading cause of death in men. Multiparametric magnetic resonance imaging (mpMRI) enables detection of the most aggressive, clinically significant PCa (csPCa) tumors that require further treatment. A suspicious region of interest (ROI) detected on mpMRI is now assigned a Prostate Imaging-Reporting and Data System (PIRADS) score to standardize interpretation of mpMRI for PCa detection. However, there is significant inter-reader variability among radiologists in PIRADS score assignment and a minimal input semi-automated artificial intelligence (AI) system is proposed to harmonize PIRADS scores with mpMRI data. Approach: The proposed deep learning model (the seed point model) uses a simulated single-click seed point as input to annotate the lesion on mpMRI. This approach is in contrast to typical medical AI-based approaches that require annotation of the complete lesion. The mpMRI data from 617 patients used in this study were prospectively collected at a major tertiary U.S. medical center. The model was trained and validated to classify whether an mpMRI image had a lesion with a PIRADS score greater than or equal to PIRADS 4. Results: The model yielded an average receiver-operator characteristic (ROC) area under the curve (ROC-AUC) of 0.704 over a 10-fold cross-validation, which is significantly higher than the previously published benchmark. Conclusions: The proposed model could aid in PIRADS scoring of mpMRI, providing second reads to promote quality as well as offering expertise in environments that lack a radiologist with training in prostate mpMRI interpretation. The model could help identify tumors with a higher PIRADS for better clinical management and treatment of PCa patients at an early stage.
- Published
- 2020
6. Estimating the Health-Related Quality of Life of Twitter Users Using Semantic Processing
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Sarma, Karthik V, Spiegel, Brennan MR, Reid, Mark W, Chen, Shawn, Merchant, Raina M, Seltzer, Emily, and Arnold, Corey W
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Clinical Research ,Behavioral and Social Science ,Good Health and Well Being ,Data Collection ,Health Status ,Humans ,Quality of Life ,Semantics ,Social Media ,Natural Language Processing ,Library and Information Studies ,Public Health and Health Services ,Medical Informatics - Abstract
Social media presents a rich opportunity to gather health information with limited intervention through the analysis of completely unstructured and unlabeled microposts. We sought to estimate the health-related quality of life (HRQOL) of Twitter users using automated semantic processing methods. We collected tweets from 878 Twitter users recruited through online solicitation and in-person contact with patients. All participants completed the four-item Centers for Disease Control Healthy Days Questionnaire at the time of enrollment and 30 days later to measure "ground truth" HRQOL. We used a combination of document frequency analysis, sentiment analysis, topic analysis, and concept mapping to extract features from tweets, which we then used to estimate dichotomized HRQOL ("high" vs. "low") using logistic regression. Binary HRQOL status was estimated with moderate performance (AUC = 0.64). This result indicates that free-range social media data only offers a window into HRQOL, but does not afford direct access to current health status.
- Published
- 2019
7. Predicting ischemic stroke tissue fate using a deep convolutional neural network on source magnetic resonance perfusion images
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Ho, Chung, Scalzo, Fabien, Sarma, Karthik V, Speier, William, El-Saden, Suzie, and Arnold, Corey
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Engineering ,Biomedical Engineering ,Brain Disorders ,Stroke ,Bioengineering ,Neurosciences ,Biomedical Imaging ,4.1 Discovery and preclinical testing of markers and technologies ,Detection ,screening and diagnosis ,deep learning ,convolutional neural network ,stroke ,perfusion imaging ,tissue fate prediction ,Clinical sciences ,Biomedical engineering - Abstract
Predicting infarct volume from magnetic resonance perfusion-weighted imaging can provide helpful information to clinicians in deciding how aggressively to treat acute stroke patients. Models have been developed to predict tissue fate, yet these models are mostly built using hand-crafted features (e.g., time-to-maximum) derived from perfusion images, which are sensitive to deconvolution methods. We demonstrate the application of deep convolution neural networks (CNNs) on predicting final stroke infarct volume using only the source perfusion images. We propose a deep CNN architecture that improves feature learning and achieves an area under the curve of 0.871±0.024 , outperforming existing tissue fate models. We further validate the proposed deep CNN with existing 2-D and 3-D deep CNNs for images/video classification, showing the importance of the proposed architecture. Our work leverages deep learning techniques in stroke tissue outcome prediction, advancing magnetic resonance imaging perfusion analysis one step closer to an operational decision support tool for stroke treatment guidance.
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- 2019
8. Path R-CNN for Prostate Cancer Diagnosis and Gleason Grading of Histological Images
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Li, Wenyuan, Li, Jiayun, Sarma, Karthik V, Ho, Chung, Shen, Shiwen, Knudsen, Beatrice S, Gertych, Arkadiusz, and Arnold, Corey W
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Information and Computing Sciences ,Engineering ,Cancer ,Urologic Diseases ,Prostate Cancer ,Aging ,Histocytochemistry ,Humans ,Image Interpretation ,Computer-Assisted ,Male ,Neoplasm Grading ,Neural Networks ,Computer ,Prostate ,Prostatic Neoplasms ,Computer-aided diagnosis ,Gleason grading ,prostate cancer ,region-based convolutional neural networks ,Nuclear Medicine & Medical Imaging ,Information and computing sciences - Abstract
Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. Here, we demonstrate a new region-based convolutional neural network framework for multi-task prediction using an epithelial network head and a grading network head. Compared with a single-task model, our multi-task model can provide complementary contextual information, which contributes to better performance. Our model is achieved a state-of-the-art performance in epithelial cells detection and Gleason grading tasks simultaneously. Using fivefold cross-validation, our model is achieved an epithelial cells detection accuracy of 99.07% with an average area under the curve of 0.998. As for Gleason grading, our model is obtained a mean intersection over union of 79.56% and an overall pixel accuracy of 89.40%.
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- 2019
9. An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies
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Li, Jiayun, Speier, William, Ho, King Chung, Sarma, Karthik V, Gertych, Arkadiusz, Knudsen, Beatrice S, and Arnold, Corey W
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Information and Computing Sciences ,Machine Learning ,Bioengineering ,Networking and Information Technology R&D (NITRD) ,Algorithms ,Deep Learning ,Humans ,Image Processing ,Computer-Assisted ,Male ,Prostate ,Prostatectomy ,Prostatic Neoplasms ,Supervised Machine Learning ,Histopathological image segmentation ,Prostate cancer ,Expectation maximization ,Semi-supervised deep learning ,Biomedical Engineering ,Clinical Sciences ,Nuclear Medicine & Medical Imaging ,Clinical sciences ,Biomedical engineering ,Computer vision and multimedia computation - Abstract
Automated Gleason grading is an important preliminary step for quantitative histopathological feature extraction. Different from the traditional task of classifying small pre-selected homogeneous regions, semantic segmentation provides pixel-wise Gleason predictions across an entire slide. Deep learning-based segmentation models can automatically learn visual semantics from data, which alleviates the need for feature engineering. However, performance of deep learning models is limited by the scarcity of large-scale fully annotated datasets, which can be both expensive and time-consuming to create. One way to address this problem is to leverage external weakly labeled datasets to augment models trained on the limited data. In this paper, we developed an expectation maximization-based approach constrained by an approximated prior distribution in order to extract useful representations from a large number of weakly labeled images generated from low-magnification annotations. This method was utilized to improve the performance of a model trained on a limited fully annotated dataset. Our semi-supervised approach trained with 135 fully annotated and 1800 weakly annotated tiles achieved a mean Jaccard Index of 49.5% on an independent test set, which was 14% higher than the initial model trained only on the fully annotated dataset.
- Published
- 2018
10. Evaluating Casama: Contextualized semantic maps for summarization of lung cancer studies
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Garcia-Gathright, Jean I, Matiasz, Nicholas J, Adame, Carlos, Sarma, Karthik V, Sauer, Lauren, Smedley, Nova F, Spiegel, Marshall L, Strunck, Jennifer, Garon, Edward B, Taira, Ricky K, Aberle, Denise R, and Bui, Alex A.T.
- Published
- 2018
11. Evaluating Casama: contextualized semantic maps for summarization of lung cancer studies
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Garcia-Gathright, Jean I., Matiasz, Nicholas J., Adame, Carlos, Sarma, Karthik V., Sauer, Lauren, Smedley, Nova F., Spiegel, Marshall L., Strunck, Jennifer, Garon, Edward B., Taira, Ricky K., Aberle, Denise R., and Bui, Alex A.T.
- Subjects
automatic summarization ,knowledge representation ,evaluation - Abstract
OBJECTIVEIt is crucial for clinicians to stay up to date on current literature in order to apply recent evidence to clinical decision making. Automatic summarization systems can help clinicians quickly view an aggregated summary of literature on a topic. Casama, a representation and summarization system based on “contextualized semantic maps,” captures the findings of biomedical studies as well as the contexts associated with patient population and study design. This paper presents a user-oriented evaluation of Casama in comparison to a context-free representation, SemRep.MATERIALS AND METHODSThe effectiveness of the representation was evaluated by presenting users with manually annotated Casama and SemRep summaries of ten articles on driver mutations in cancer. Automatic annotations were evaluated on a collection of articles on EGFR mutation in lung cancer. Seven users completed a questionnaire rating the summarization quality for various topics and applications.RESULTSCasama had higher median scores than SemRep for the majority of the topics (p ≤ 0.00032), all of the applications (p ≤ 0.00089), and in overall summarization quality (p ≤ 1.5e-05). Casama’s manual annotations outperformed Casama’s automatic annotations (p = 0.00061).DISCUSSIONCasama performed particularly well in the representation of strength of evidence, which was highly rated both quantitatively and qualitatively. Users noted that Casama’s less granular, more targeted representation improved usability compared to SemRep.CONCLUSIONThis evaluation demonstrated the benefits of a contextualized representation for summarizing biomedical literature on cancer. Iteration on specific areas of Casama’s representation, further development of its algorithms, and a clinically-oriented evaluation are warranted.
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- 2017
12. A Multi-scale U-Net for Semantic Segmentation of Histological Images from Radical Prostatectomies.
- Author
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Li, Jiayun, Sarma, Karthik V, Chung Ho, King, Gertych, Arkadiusz, Knudsen, Beatrice S, and Arnold, Corey W
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Biomedical and Clinical Sciences ,Clinical Sciences ,Oncology and Carcinogenesis ,Prostate Cancer ,Cancer ,Urologic Diseases ,Aging ,Deep Learning ,Diagnosis ,Computer-Assisted ,Humans ,Machine Learning ,Male ,Neoplasm Grading ,Prostate ,Prostatectomy ,Prostatic Neoplasms ,Risk Assessment ,Semantics ,Support Vector Machine - Abstract
Gleason grading of histological images is important in risk assessment and treatment planning for prostate cancer patients. Much research has been done in classifying small homogeneous cancer regions within histological images. However, semi-supervised methods published to date depend on pre-selected regions and cannot be easily extended to an image of heterogeneous tissue composition. In this paper, we propose a multi-scale U-Net model to classify images at the pixel-level using 224 histological image tiles from radical prostatectomies of 20 patients. Our model was evaluated by a patient-based 10-fold cross validation, and achieved a mean Jaccard index of 65.8% across 4 classes (stroma, Gleason 3, Gleason 4 and benign glands), and 75.5% for 3 classes (stroma, benign glands, prostate cancer), outperforming other methods.
- Published
- 2017
13. Integrating Battlefield Documentation into Virtual Reality Medical Simulation Training: Virtual Battlefield Assisted Trauma Distributed Observation Kit (BATDOK)
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Sarma, Karthik V, primary, Barrie, Michael G, additional, Dorsch, John R, additional, Andre, Tanner W, additional, Polson, Jennifer S, additional, Ribeira, Rosie J, additional, Andre, Tyler B, additional, and Ribeira, Ryan J, additional
- Published
- 2023
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14. 4.29 Can Artificial Intelligence Make the Diagnosis? Evaluating the Accuracy of Large Language Models in Diagnosing Child and Adolescent Psychiatry Clinical Cases.
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Hanss, Kaitlin, Sarma, Karthik V., Halls, Andrew, Gorrell, Sasha, and Reilly, Erin
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- 2024
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15. Supplemental Online Material from A domain-level DNA strand displacement reaction enumerator allowing arbitrary non-pseudoknotted secondary structures
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Badelt, Stefan, Grun, Casey, Sarma, Karthik V., Wolfe, Brian, Shin, Seung Woo, and Winfree, Erik
- Abstract
Information technologies enable programmers and engineers to design and synthesize systems of startling complexity that nonetheless behave as intended. This mastery of complexity is made possible by a hierarchy of formal abstractions that span from high-level programming languages down to low-level implementation specifications, with rigorous connections between the levels. DNA nanotechnology presents us with a new molecular information technology whose potential has not yet been fully unlocked in this way. Developing an effective hierarchy of abstractions may be critical for increasing the complexity of programmable DNA systems. Here, we build on prior practice to provide a new formalization of ‘domain-level’ representations of DNA strand displacement systems that has a natural connection to nucleic acid biophysics while still being suitable for formal analysis. Enumeration of unimolecular and bimolecular reactions provides a semantics for programmable molecular interactions, with kinetics given by an approximate biophysical model. Reaction condensation provides a tractable simplification of the detailed reactions that respects overall kinetic properties. The applicability and accuracy of the model is evaluated across a wide range of engineered DNA strand displacement systems. Thus, our work can serve as an interface between lower-level DNA models that operate at the nucleotide sequence level, and high-level chemical reaction network models that operate at the level of interactions between abstract species.
- Published
- 2020
- Full Text
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16. Clinical Uncertainties, Health Service Challenges, and Ethical Complexities of HIV “Test-and-Treat”: A Systematic Review
- Author
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Kulkarni, Sonali P, Shah, Kavita R, Sarma, Karthik V, and Mahajan, Anish P
- Published
- 2013
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17. A domain-level DNA strand displacement reaction enumerator allowing arbitrary non-pseudoknotted secondary structures
- Author
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Badelt, Stefan, primary, Grun, Casey, additional, Sarma, Karthik V., additional, Wolfe, Brian, additional, Shin, Seung Woo, additional, and Winfree, Erik, additional
- Published
- 2020
- Full Text
- View/download PDF
18. Predicting ischemic stroke tissue fate using a deep convolutional neural network on source magnetic resonance perfusion images.
- Author
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Ho, King Chung, Ho, King Chung, Scalzo, Fabien, Sarma, Karthik V, Speier, William, El-Saden, Suzie, Arnold, Corey, Ho, King Chung, Ho, King Chung, Scalzo, Fabien, Sarma, Karthik V, Speier, William, El-Saden, Suzie, and Arnold, Corey
- Abstract
Predicting infarct volume from magnetic resonance perfusion-weighted imaging can provide helpful information to clinicians in deciding how aggressively to treat acute stroke patients. Models have been developed to predict tissue fate, yet these models are mostly built using hand-crafted features (e.g., time-to-maximum) derived from perfusion images, which are sensitive to deconvolution methods. We demonstrate the application of deep convolution neural networks (CNNs) on predicting final stroke infarct volume using only the source perfusion images. We propose a deep CNN architecture that improves feature learning and achieves an area under the curve of 0.871±0.024 , outperforming existing tissue fate models. We further validate the proposed deep CNN with existing 2-D and 3-D deep CNNs for images/video classification, showing the importance of the proposed architecture. Our work leverages deep learning techniques in stroke tissue outcome prediction, advancing magnetic resonance imaging perfusion analysis one step closer to an operational decision support tool for stroke treatment guidance.
- Published
- 2019
19. PD64-05 A MACHINE LEARNING MODEL FOR PREDICTING CANCER PRESENCE IN PROSTATE BIOPSY TARGETS USING MRI
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Raman*, Alex G., primary, Sarma, Karthik V., additional, Shi, Wen, additional, Zhou, Steve, additional, Priester, Alan M., additional, Natarajan, Shyam, additional, Speier, William, additional, Raman, Steven S., additional, Marks, Leonard S., additional, and Arnold, Corey W., additional
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- 2019
- Full Text
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20. An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies
- Author
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Li, Jiayun, primary, Speier, William, additional, Ho, King Chung, additional, Sarma, Karthik V., additional, Gertych, Arkadiusz, additional, Knudsen, Beatrice S., additional, and Arnold, Corey W., additional
- Published
- 2018
- Full Text
- View/download PDF
21. Estimating the Health-Related Quality of Life of Twitter Users Using Semantic Processing.
- Author
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Sarma, Karthik V., Spiegel, Brennan M. R., Reid, Mark W., Chen, Shawn, Merchant, Raina M., Seltzer, Emily, and Arnold, Corey W.
- Subjects
MEDICAL informatics ,SOCIAL media ,QUALITY of life ,NATURAL language processing - Abstract
Social media presents a rich opportunity to gather health information with limited intervention through the analysis of completely unstructured and unlabeled microposts. We sought to estimate the health-related quality of life (HRQOL) of Twitter users using automated semantic processing methods. We collected tweets from 878 Twitter users recruited through online solicitation and in-person contact with patients. All participants completed the four-item Centers for Disease Control Healthy Days Questionnaire at the time of enrollment and 30 days later to measure “ground truth” HRQOL. We used a combination of document frequency analysis, sentiment analysis, topic analysis, and concept mapping to extract features from tweets, which we then used to estimate dichotomized HRQOL (“high” vs. “low”) using logistic regression. Binary HRQOL status was estimated with moderate performance (AUC=0.64). This result indicates that free-range social media data only offers a window into HRQOL, but does not afford direct access to current health status. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
22. A temporal deep learning approach for MR perfusion parameter estimation in stroke
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Ho, King Chung, primary, Scalzo, Fabien, additional, Sarma, Karthik V., additional, El-Saden, Suzie, additional, and Arnold, Corey W., additional
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- 2016
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- View/download PDF
23. Clinical Uncertainties, Health Service Challenges, and Ethical Complexities of HIV 'Test-and-Treat': A Systematic Review.
- Author
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Prakash Kulkarni, Sonali, Shah, Kavita R., Sarma, Karthik V., and Mahajan, Anish P.
- Abstract
Despite the HIV 'test-and-treat' strategy's promise, questions about its clinical rationale, operational feasibility, and ethical appropriateness have led to vigorous debate in the global HIV community. We performed a systematic review of the literature published between January 2009 and May 2012 using PubMed, SCOPUS, Global Health, Web of Science, BIOSIS, Cochrane CENTRAL, EBSCO Africa-Wide Information, and EBSCO CINAHL Plus databases to summarize clinical uncertainties, health service challenges, and ethical complexities that may affect the test-and-treat strategy's success. A thoughtful approach to research and implementation to address clinical and health service questions and meaningful community engagement regarding ethical complexities may bring us closer to safe, feasible, and effective test-and-treat implementation. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
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24. Estimating the Health-Related Quality of Life of Twitter Users Using Semantic Processing.
- Author
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Sarma KV, Spiegel BMR, Reid MW, Chen S, Merchant RM, Seltzer E, and Arnold CW
- Subjects
- Data Collection, Health Status, Humans, Quality of Life, Semantics, Social Media
- Abstract
Social media presents a rich opportunity to gather health information with limited intervention through the analysis of completely unstructured and unlabeled microposts. We sought to estimate the health-related quality of life (HRQOL) of Twitter users using automated semantic processing methods. We collected tweets from 878 Twitter users recruited through online solicitation and in-person contact with patients. All participants completed the four-item Centers for Disease Control Healthy Days Questionnaire at the time of enrollment and 30 days later to measure "ground truth" HRQOL. We used a combination of document frequency analysis, sentiment analysis, topic analysis, and concept mapping to extract features from tweets, which we then used to estimate dichotomized HRQOL ("high" vs. "low") using logistic regression. Binary HRQOL status was estimated with moderate performance (AUC = 0.64). This result indicates that free-range social media data only offers a window into HRQOL, but does not afford direct access to current health status.
- Published
- 2019
- Full Text
- View/download PDF
25. A Multi-scale U-Net for Semantic Segmentation of Histological Images from Radical Prostatectomies.
- Author
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Li J, Sarma KV, Chung Ho K, Gertych A, Knudsen BS, and Arnold CW
- Subjects
- Deep Learning, Humans, Male, Prostate pathology, Prostatic Neoplasms surgery, Risk Assessment, Semantics, Support Vector Machine, Diagnosis, Computer-Assisted, Machine Learning, Neoplasm Grading, Prostatectomy, Prostatic Neoplasms pathology
- Abstract
Gleason grading of histological images is important in risk assessment and treatment planning for prostate cancer patients. Much research has been done in classifying small homogeneous cancer regions within histological images. However, semi-supervised methods published to date depend on pre-selected regions and cannot be easily extended to an image of heterogeneous tissue composition. In this paper, we propose a multi-scale U-Net model to classify images at the pixel-level using 224 histological image tiles from radical prostatectomies of 20 patients. Our model was evaluated by a patient-based 10-fold cross validation, and achieved a mean Jaccard index of 65.8% across 4 classes (stroma, Gleason 3, Gleason 4 and benign glands), and 75.5% for 3 classes (stroma, benign glands, prostate cancer), outperforming other methods.
- Published
- 2018
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