1 citations · 2 across the 5 of their papers we have counts for
5 papers
From Data to Diagnosis: A Large, Comprehensive Bone Marrow Dataset and AI Methods for Childhood Leukemia Prediction
Henning Höfener, Farina Kock, Martina Pontones +9
Leukemia diagnosis primarily relies on manual microscopic analysis of bone marrow morphology supported by additional laboratory parameters, making it complex and time consuming. Wh…
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…
Medical Image De-Identification Benchmark Challenge
Linmin Pei, Granger Sutton, Michael Rutherford +67
The de-identification (deID) of protected health information (PHI) and personally identifiable information (PII) is a fundamental requirement for sharing medical images, particular…
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…
Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations
Deepa Krishnaswamy, Dennis Bontempi, Vamsi Thiriveedhi +6
Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived featur…