184 citations · 532 across the 20 of their papers we have counts for
13 papers · 1 filter
Nucleus-aware Self-supervised Pretraining Using Unpaired Image-to-image Translation for Histopathology Images
Zhiyun Song, Penghui Du, Junpeng Yan +5
Self-supervised pretraining attempts to enhance model performance by obtaining effective features from unlabeled data, and has demonstrated its effectiveness in the field of histop…
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation
Han Liu, Hao Li, Xing Yao +6
Medical image segmentation is a critical task in medical image analysis. In recent years, deep learning based approaches have shown exceptional performance when trained on a fully-…
Zero-shot Nuclei Detection via Visual-Language Pre-trained Models
Yongjian Wu, Yang Zhou, Jiya Saiyin +5
Large-scale visual-language pre-trained models (VLPM) have proven their excellent performance in downstream object detection for natural scenes. However, zero-shot nuclei detection…
Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation
Yang Zhou, Yongjian Wu, Zihua Wang +5
Nuclei instance segmentation on histopathology images is of great clinical value for disease analysis. Generally, fully-supervised algorithms for this task require pixel-wise manua…
Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation
Han Liu, Yubo Fan, Ipek Oguz +1
Automatic segmentation of vestibular schwannoma (VS) and cochlea from magnetic resonance imaging can facilitate VS treatment planning. Unsupervised segmentation methods have shown…
Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ Segmentation
Ho Hin Lee, Yucheng Tang, Han Liu +7
Recent studies have demonstrated the superior performance of introducing ``scan-wise" contrast labels into contrastive learning for multi-organ segmentation on multi-phase computed…
Transformer based multiple instance learning for weakly supervised histopathology image segmentation
Ziniu Qian, Kailu Li, Maode Lai +4
Hispathological image segmentation algorithms play a critical role in computer aided diagnosis technology. The development of weakly supervised segmentation algorithm alleviates th…
Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusion
Han Liu, Yubo Fan, Can Cui +3
Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic resonance imaging (MRI) are critical to VS treatment planning. Although supervised…
Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks
Bo Hu, Ye Tang, Eric I-Chao Chang +3
The visual attributes of cells, such as the nuclear morphology and chromatin openness, are critical for histopathology image analysis. By learning cell-level visual representation,…
Unsupervised End-to-end Learning for Deformable Medical Image Registration
Siyuan Shan, Wen Yan, Xiaoqing Guo +3
We propose a registration algorithm for 2D CT/MRI medical images with a new unsupervised end-to-end strategy using convolutional neural networks. The contributions of our algorithm…
Learning Multi-level Features For Sensor-based Human Action Recognition
Yan Xu, Zhengyang Shen, Xin Zhang +5
This paper proposes a multi-level feature learning framework for human action recognition using a single body-worn inertial sensor. The framework consists of three phases, respecti…
Gland Instance Segmentation Using Deep Multichannel Neural Networks
Yan Xu, Yang Li, Yipei Wang +4
Objective: A new image instance segmentation method is proposed to segment individual glands (instances) in colon histology images. This process is challenging since the glands not…
Gland Instance Segmentation by Deep Multichannel Neural Networks
Yan Xu, Yang Li, Mingyuan Liu +4
In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images. This is a task called instance segmentatio…