Eva Prakash
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View article: Improving the Performance of Radiology Report De-identification with Large-Scale Training and Benchmarking Against Cloud Vendor Methods
Improving the Performance of Radiology Report De-identification with Large-Scale Training and Benchmarking Against Cloud Vendor Methods Open
Objective: To enhance automated de-identification of radiology reports by scaling transformer-based models through extensive training datasets and benchmarking performance against commercial cloud vendor systems for protected health inform…
View article: Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for Medical Image Analysis
Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for Medical Image Analysis Open
Purpose: To explore best-practice approaches for generating synthetic chest X-ray images and augmenting medical imaging datasets to optimize the performance of deep learning models in downstream tasks like classification and segmentation. …
View article: Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation
Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation Open
The high cost of creating pixel-by-pixel gold-standard labels, limited expert availability, and presence of diverse tasks make it challenging to generate segmentation labels to train deep learning models for medical imaging tasks. In this …
View article: Measuring Compositional Consistency for Video Question Answering
Measuring Compositional Consistency for Video Question Answering Open
Recent video question answering benchmarks indicate that state-of-the-art models struggle to answer compositional questions. However, it remains unclear which types of compositional reasoning cause models to mispredict. Furthermore, it is …
View article: Towards More Realistic Simulated Datasets for Benchmarking Deep Learning Models in Regulatory Genomics
Towards More Realistic Simulated Datasets for Benchmarking Deep Learning Models in Regulatory Genomics Open
Deep neural networks and support vector machines have been shown to accurately predict genome-wide signals of regulatory activity from raw DNA sequences. These models are appealing in part because they can learn predictive DNA sequence fea…
View article: GkmExplain: fast and accurate interpretation of nonlinear gapped <i>k</i> -mer SVMs
GkmExplain: fast and accurate interpretation of nonlinear gapped <i>k</i> -mer SVMs Open
Summary Support Vector Machines with gapped k-mer kernels (gkm-SVMs) have been used to learn predictive models of regulatory DNA sequence. However, interpreting predictive sequence patterns learned by gkm-SVMs can be challenging. Existing …