52 citations · 66 across the 14 of their papers we have counts for
6 papers · 1 filter
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization
Chuyan Zhang, Yuncheng Yang, Hao Zheng +1
Driven by the latest trend towards self-supervised learning (SSL), the paradigm of "pretraining-then-finetuning" has been extensively explored to enhance the performance of clinica…
Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image Segmentation
Yuncheng Yang, Meng Wei, Junjun He +3
Transfer learning is a critical technique in training deep neural networks for the challenging medical image segmentation task that requires enormous resources. With the abundance…
Accurate Airway Tree Segmentation in CT Scans via Anatomy-aware Multi-class Segmentation and Topology-guided Iterative Learning
Puyang Wang, Dazhou Guo, Dandan Zheng +8
Intrathoracic airway segmentation in computed tomography (CT) is a prerequisite for various respiratory disease analyses such as chronic obstructive pulmonary disease (COPD), asthm…
CDFI: Cross Domain Feature Interaction for Robust Bronchi Lumen Detection
Jiasheng Xu, Tianyi Zhang, Yangqian Wu +3
Endobronchial intervention is increasingly used as a minimally invasive means for the treatment of pulmonary diseases. In order to reduce the difficulty of manipulation in complex…
STU-Net: Scalable and Transferable Medical Image Segmentation Models Empowered by Large-Scale Supervised Pre-training
Ziyan Huang, Haoyu Wang, Zhongying Deng +8
Large-scale models pre-trained on large-scale datasets have profoundly advanced the development of deep learning. However, the state-of-the-art models for medical image segmentatio…
Learning with Explicit Shape Priors for Medical Image Segmentation
Xin You, Junjun He, Jie Yang +1
Medical image segmentation is a fundamental task for medical image analysis and surgical planning. In recent years, UNet-based networks have prevailed in the field of medical image…