Publications (7)
Masked Angle-Aware Autoencoder for Remote Sensing Images
Zhihao Li, Biao Hou, Siteng Ma +4
To overcome the inherent domain gap between remote sensing (RS) images and natural images, some self-supervised representation learning methods have made promising progress. Howeve…
Adaptive Nonlinear Latent Transformation for Conditional Face Editing
Zhizhong Huang, Siteng Ma, Junping Zhang +1
Recent works for face editing usually manipulate the latent space of StyleGAN via the linear semantic directions. However, they usually suffer from the entanglement of facial attri…
Entropy-Guided Agreement-Diversity: A Semi-Supervised Active Learning Framework for Fetal Head Segmentation in Ultrasound
Fangyijie Wang, Siteng Ma, Guénolé Silvestre +1
Fetal ultrasound (US) data is often limited due to privacy and regulatory restrictions, posing challenges for training deep learning (DL) models. While semi-supervised learning (SS…
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…
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…
HDRFace: Rethinking Face Restoration with High-Dimensional Representation
Zirui Wang, Xianhui Lin, Yi Dong +7
Face restoration under complex degradations still remains an ill-posed inverse problem due to severe information loss. Although diffusion models benefit from strong generative prio…
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…