1 citations · 2 across the 6 of their papers we have counts for
6 papers
Annotating anatomy and pathology in the National Lung Screening Trial computed tomography images
Deepa Krishnaswamy, Vamsi Thiriveedhi, Suraj Pai +6
Large-scale public medical imaging datasets contribute critically to translational research. When accompanied by rich clinical and multi-omics data, they can stimulate exploratory…
In search of truth: Evaluating concordance of AI-based anatomy segmentation models
Lena Giebeler, Deepa Krishnaswamy, David Clunie +9
Purpose AI-based methods for anatomy segmentation can help automate characterization of large imaging datasets. The growing number of similar in functionality models raises the cha…
Medical Image De-Identification Resources: Synthetic DICOM Data and Tools for Validation
Michael W. Rutherford, Tracy Nolan, Linmin Pei +10
Medical imaging research increasingly depends on large-scale data sharing to promote reproducibility and train Artificial Intelligence (AI) models. Ensuring patient privacy remains…
Rule-based outlier detection of AI-generated anatomy segmentations
Deepa Krishnaswamy, Vamsi Krishna Thiriveedhi, Cosmin Ciausu +4
There is a dire need for medical imaging datasets with accompanying annotations to perform downstream patient analysis. However, it is difficult to manually generate these annotati…
Automatic classification of prostate MR series type using image content and metadata
Deepa Krishnaswamy, Bálint Kovács, Stefan Denner +6
With the wealth of medical image data, efficient curation is essential. Assigning the sequence type to magnetic resonance images is necessary for scientific studies and artificial…
AI-Generated Annotations Dataset for Diverse Cancer Radiology Collections in NCI Image Data Commons
Gowtham Krishnan Murugesan, Diana McCrumb, Mariam Aboian +10
The National Cancer Institute (NCI) Image Data Commons (IDC) offers publicly available cancer radiology collections for cloud computing, crucial for developing advanced imaging too…