59 citations · 65 across the 5 of their papers we have counts for
5 papers
LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification
Reuben Dorent, Roya Khajavi, Tagwa Idris +24
Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentati…
TSynD: Targeted Synthetic Data Generation for Enhanced Medical Image Classification
Joshua Niemeijer, Jan Ehrhardt, Hristina Uzunova +1
The usage of medical image data for the training of large-scale machine learning approaches is particularly challenging due to its scarce availability and the costly generation of…
LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node Atlas
Sofija Engelson, Jan Ehrhardt, Timo Kepp +2
The evaluation of lymph node metastases plays a crucial role in achieving precise cancer staging, influencing subsequent decisions regarding treatment options. Lymph node detection…
Memory-efficient GAN-based Domain Translation of High Resolution 3D Medical Images
Hristina Uzunova, Jan Ehrhardt, Heinz Handels
Generative adversarial networks (GANs) are currently rarely applied on 3D medical images of large size, due to their immense computational demand. The present work proposes a multi…
Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images
Hristina Uzunova, Jan Ehrhardt, Fabian Jacob +2
Currently generative adversarial networks (GANs) are rarely applied to medical images of large sizes, especially 3D volumes, due to their large computational demand. We propose a n…