21 citations · 31 across the 4 of their papers we have counts for
5 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…
Whole Slide Concepts: A Supervised Foundation Model For Pathological Images
Till Nicke, Daniela Schacherer, Jan Raphael Schäfer +6
Foundation models (FMs) are transforming computational pathology by offering new ways to analyze histopathology images. However, FMs typically require weeks of training on large da…
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…
Evaluating Generic Auto-ML Tools for Computational Pathology
Lars Ole Schwen, Daniela Schacherer, Christian Geißler +1
Image analysis tasks in computational pathology are commonly solved using convolutional neural networks (CNNs). The selection of a suitable CNN architecture and hyperparameters is…