44 citations · 57 across the 5 of their papers we have counts for
5 papers
Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey
Mischa Dombrowski, Andrea Prenner, Bernhard Kainz
Machine Learning (ML) models have gained popularity in medical imaging analysis given their expert level performance in many medical domains. To enhance the trustworthiness, accept…
Generalist Foundation Models from a Multimodal Dataset for 3D Computed Tomography
Ibrahim Ethem Hamamci, Sezgin Er, Chenyu Wang +27
Advancements in medical imaging AI, particularly in 3D imaging, have been limited due to the scarcity of comprehensive datasets. We introduce CT-RATE, a public dataset that pairs 3…
Realistic Data Enrichment for Robust Image Segmentation in Histopathology
Sarah Cechnicka, James Ball, Hadrien Reynaud +3
Poor performance of quantitative analysis in histopathological Whole Slide Images (WSI) has been a significant obstacle in clinical practice. Annotating large-scale WSIs manually i…
Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging
Qingjie Meng, Matthew Sinclair, Veronika Zimmer +11
Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a stand…
DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks
Martin Rajchl, Matthew C. H. Lee, Ozan Oktay +8
In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled with bounding box annotations. It extends the approach of the w…