Radiotherapy AI: More patient images don't beat smarter data tricks
A new study challenges a common assumption in medical AI: that feeding deep learning models multiple images per patient dramatically improves cancer treatment accuracy. Researchers found standard data augmentation techniques outperform the multi-image approach, suggesting hospitals can cut annotation costs without sacrificing segmentation precision in adaptive radiotherapy workflows.
Originaltitel: Comparing multi-image and image augmentation strategies for deep learning-based prostate segmentation
<p>During MR-Linac-based adaptive radiotherapy, multiple images are acquired per patient. These can be applied in training deep learning networks to reduce annotation efforts. This study examined the advantage of using multiple versus single images for prostate treatment segmentation. Findings indicate minimal improvement in DICE and Hausdorff 95% metrics with multiple images. Maximum difference was seen for the rectum in the low data regime, training with images from five patients. Utilizing a 2D U -net resulted in DICE values of 0.80/0.83 when including 1/5 images per patient, respectively. Including more patients in training reduced the difference. Standard augmentation methods remained more effective.</p>