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cs.CV2025
Is Complete Labeling Necessary? Understanding Active Learning in Longitudinal Medical Imaging
Siteng Ma, Honghui Du, Prateek Mathur +4
Detecting changes in longitudinal medical imaging using deep learning requires a substantial amount of accurately labeled data. However, labeling these images is notably more costl…
cs.CV2025★ 1 cited
Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey
Siteng Ma, Honghui Du, Yu An +5
Deep learning has achieved significant breakthroughs in medical imaging, but these advancements are often dependent on large, well-annotated datasets. However, obtaining such datas…
cs.CV2024★ 1 cited
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation
Siteng Ma, Haochang Wu, Aonghus Lawlor +1
Active learning (AL) has found wide applications in medical image segmentation, aiming to alleviate the annotation workload and enhance performance. Conventional uncertainty-based…