34 citations · 93 across the 8 of their papers we have counts for
9 papers
Sharing standardized image-derived data in computational pathology using DICOM
Daniela P. Schacherer, Christopher P. Bridge, David Clunie +13
Development and evaluation of computational pathology methods require access to large and diverse datasets. Over the past decade, various initiatives invested significantly into co…
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 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…
Unlocking the Potential of Digital Pathology: Novel Baselines for Compression
Maximilian Fischer, Peter Neher, Peter Schüffler +13
Digital pathology offers a groundbreaking opportunity to transform clinical practice in histopathological image analysis, yet faces a significant hurdle: the substantial file sizes…
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…
Report of the Medical Image De-Identification (MIDI) Task Group -- Best Practices and Recommendations
David A. Clunie, Adam Flanders, Adam Taylor +16
This report addresses the technical aspects of de-identification of medical images of human subjects and biospecimens, such that re-identification risk of ethical, moral, and legal…
The NCI Imaging Data Commons as a platform for reproducible research in computational pathology
Daniela P. Schacherer, Markus D. Herrmann, David A. Clunie +7
Background and Objectives: Reproducibility is a major challenge in developing machine learning (ML)-based solutions in computational pathology (CompPath). The NCI Imaging Data Comm…
Slim: interoperable slide microscopy viewer and annotation tool for imaging data science and computational pathology
Chris Gorman, Davide Punzo, Igor Octaviano +6
The exchange of large and complex slide microscopy imaging data in biomedical research and pathology practice is impeded by a lack of data standardization and interoperability, whi…
Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology
Christopher P. Bridge, Chris Gorman, Steven Pieper +6
Machine learning is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but their lack of interoperabil…