activity
20222024
most citedVideo4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-based Video Classification Frameworks

2 citations · 5 across the 7 of their papers we have counts for

collaborators

6 papers

cs.CV20231 cited

CUPre: Cross-domain Unsupervised Pre-training for Few-Shot Cell Segmentation

Weibin Liao, Xuhong Li, Qingzhong Wang +3

While pre-training on object detection tasks, such as Common Objects in Contexts (COCO) [1], could significantly boost the performance of cell segmentation, it still consumes on ma…

cs.CV20232 cited

Video4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-based Video Classification Frameworks

Yuxuan Zhang, Qingzhong Wang, Jiang Bian +4

To address the problem of medical image recognition, computer vision techniques like convolutional neural networks (CNN) are frequently used. Recently, 3D CNN-based models dominate…

cs.CV2022

Multi-Scale Multi-Target Domain Adaptation for Angle Closure Classification

Zhen Qiu, Yifan Zhang, Fei Li +3

Deep learning (DL) has made significant progress in angle closure classification with anterior segment optical coherence tomography (AS-OCT) images. These AS-OCT images are often a…

eess.IV20221 cited

Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle

Junde Wu, Huihui Fang, Hoayi Xiong +5

On the medical images, many of the tissues/lesions may be ambiguous. That is why the medical segmentation is typically annotated by a group of clinical experts to mitigate the pers…

cs.CV2022

Dataset and Evaluation algorithm design for GOALS Challenge

Huihui Fang, Fei Li, Huazhu Fu +3

Glaucoma causes irreversible vision loss due to damage to the optic nerve, and there is no cure for glaucoma.OCT imaging modality is an essential technique for assessing glaucomato…

cs.CV20221 cited

Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation

Yanwu Xu, Shaoan Xie, Maxwell Reynolds +3

An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models. Domain Generaliz…