1 citations · 1 across the 4 of their papers we have counts for
4 papers
Less Annotation, More Interpretation: Prior-Guided Concept Bottleneck Models for Interpretable Cancer Imaging Diagnosis
Baoqiang Ma, Kenneth Gilhuijs
Concept bottleneck models (CBMs) can improve the transparency of cancer image diagnostic prediction by expressing predictions through radiological concepts. However, their dependen…
Ranking XAI Methods for Head and Neck Cancer Outcome Prediction
Baoqiang Ma, Djennifer K. Madzia-Madzou, Rosa C. J. Kraaijveld +1
For head and neck cancer (HNC) patients, prognostic outcome prediction can support personalized treatment strategy selection. Improving prediction performance of HNC outcomes has b…
PR3DICTR: A modular AI framework for medical 3D image-based detection and outcome prediction
Daniel C. MacRae, Luuk van der Hoek, Robert van der Wal +5
Three-dimensional medical image data and computer-aided decision making, particularly using deep learning, are becoming increasingly important in the medical field. To aid in these…
MRI-based Head and Neck Tumor Segmentation Using nnU-Net with 15-fold Cross-Validation Ensemble
Frank N. Mol, Luuk van der Hoek, Baoqiang Ma +6
The superior soft tissue differentiation provided by MRI may enable more accurate tumor segmentation compared to CT and PET, potentially enhancing adaptive radiotherapy treatment p…