2.1k citations · 4k across the 21 of their papers we have counts for
8 papers · 1 filter
Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification
Junfei Xiao, Yutong Bai, Alan Yuille +1
Vision Transformer (ViT) has become one of the most popular neural architectures due to its great scalability, computational efficiency, and compelling performance in many vision t…
Making Your First Choice: To Address Cold Start Problem in Vision Active Learning
Liangyu Chen, Yutong Bai, Siyu Huang +4
Active learning promises to improve annotation efficiency by iteratively selecting the most important data to be annotated first. However, we uncover a striking contradiction to th…
MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging
Yixiong Chen, Li Liu, Jingxian Li +3
In medical image analysis, transfer learning is a powerful method for deep neural networks (DNNs) to generalize well on limited medical data. Prior efforts have focused on developi…
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection
Tiange Xiang, Yixiao Zhang, Yongyi Lu +4
Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. To expl…
Label-Assemble: Leveraging Multiple Datasets with Partial Labels
Mintong Kang, Bowen Li, Zengle Zhu +4
The success of deep learning relies heavily on large labeled datasets, but we often only have access to several small datasets associated with partial labels. To address this probl…
Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learning
Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Zongwei Zhou +2
This paper introduces a new concept called "transferable visual words" (TransVW), aiming to achieve annotation efficiency for deep learning in medical image analysis. Medical imagi…
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh +1
In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network wher…
Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images
Hongkai Wang, Zongwei Zhou, Yingci Li +5
The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastas…