Publications by authors named "Mehak Aggarwal"

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g.

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To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score).

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Purpose: To evaluate the trustworthiness of saliency maps for abnormality localization in medical imaging.

Materials And Methods: Using two large publicly available radiology datasets (Society for Imaging Informatics in Medicine-American College of Radiology Pneumothorax Segmentation dataset and Radiological Society of North America Pneumonia Detection Challenge dataset), the performance of eight commonly used saliency map techniques were quantified in regard to localization utility (segmentation and detection), sensitivity to model weight randomization, repeatability, and reproducibility. Their performances versus baseline methods and localization network architectures were compared, using area under the precision-recall curve (AUPRC) and structural similarity index measure (SSIM) as metrics.

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Article Synopsis
  • The study aimed to enhance a deep learning model for assessing the severity of COVID-19 lung disease in chest radiographs (CXRs) by tuning it with outpatient data.
  • The model was evaluated on CXRs from various populations in the U.S. and Brazil, showing improved performance, particularly in U.S. hospitalized patients.
  • The findings suggest that training the model with data from a different patient group improved its effectiveness and allowed it to generalize well across multiple patient populations.
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Tacrolimus exhibits high inter-patient pharmacokinetics (PK) variability, as well as a narrow therapeutic index, and therefore requires therapeutic drug monitoring. Germline mutations in cytochrome P450 isoforms 4 and 5 genes () and the ATP-binding cassette B1 gene () may contribute to interindividual tacrolimus PK variability, which may impact clinical outcomes among allogeneic hematopoietic stem cell transplantation (HSCT) patients. In this study, 252 adult patients who received tacrolimus for acute graft versus host disease (aGVHD) prophylaxis after allogeneic HSCT were genotyped to evaluate if germline genetic variants associated with tacrolimus PK and pharmacodynamic (PD) variability.

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