6 papers · 1 filter
SUGFW+: An Uncertainty-guided Feature Weighting Framework for Cold Start Active Adaptation of SAM in Medical Image Segmentation
Xiaochuan Ma, Ning Zhu, Jia Fu +5
Cold Start Active Learning (CSAL) is important in improving the performance of a medical image segmentation model with low annotation budget by querying a small subset for annotati…
RAFM: Retrieval-Augmented Flow Matching for Unpaired CBCT-to-CT Translation
Xianhao Zhou, Jianghao Wu, Lanfeng Zhong +4
Cone-beam CT (CBCT) is routinely acquired in radiotherapy but suffers from severe artifacts and unreliable Hounsfield Unit (HU) values, limiting its direct use for dose calculation…
MetaSSL: A General Heterogeneous Loss for Semi-Supervised Medical Image Segmentation
Weiren Zhao, Lanfeng Zhong, Xin Liao +4
Semi-Supervised Learning (SSL) is important for reducing the annotation cost for medical image segmentation models. State-of-the-art SSL methods such as Mean Teacher, FixMatch and…
OpenPath: Open-Set Active Learning for Pathology Image Classification via Pre-trained Vision-Language Models
Lanfeng Zhong, Xin Liao, Shichuan Zhang +2
Pathology image classification plays a crucial role in accurate medical diagnosis and treatment planning. Training high-performance models for this task typically requires large-sc…
VLM-CPL: Consensus Pseudo Labels from Vision-Language Models for Annotation-Free Pathological Image Classification
Lanfeng Zhong, Zongyao Huang, Yang Liu +4
Classification of pathological images is the basis for automatic cancer diagnosis. Despite that deep learning methods have achieved remarkable performance, they heavily rely on lab…
Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions
Lanfeng Zhong, Xin Liao, Shaoting Zhang +1
Segmentation of pathological images is a crucial step for accurate cancer diagnosis. However, acquiring dense annotations of such images for training is labor-intensive and time-co…