Author name cluster

Chaoli Wang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

EAAI Journal 2024 Journal Article

A single frame and multi-frame joint network for 360-degree panorama video super-resolution

Hongying Liu
Wanhao Ma
Zhubo Ruan
Chaowei Fang
Fanhua Shang
Yuanyuan Liu
Lijun Wang
Chaoli Wang

Spherical videos, also known as 360-degree (panorama) videos, can be viewed with various virtual reality devices such as computers and head-mounted displays. They attract a large amount of interest since awesome immersion can be experienced when watching spherical videos. However, capturing, storing and transmitting high-resolution spherical videos are extremely expensive, and the low-resolution ones are widely available. In this paper, we propose a novel single-frame and multi-frame joint network (SMFN) for recovering high-resolution spherical videos from low-resolution observations. To take advantage of pixel-level inter-frame consistency, we use deformable convolutions to eliminate the motion difference between feature maps of the target frame and its neighboring frames. A mixed attention mechanism is devised to enhance the feature representation capability. The dual learning strategy is presented to constrain the space of solutions so that a better solution can be found. A new loss function based on the weighted mean squared error is proposed to emphasize the super-resolution of the equatorial regions. This is the first attempt to settle the super-resolution of spherical videos, and we collect a new dataset from the Internet, MiG panorama video, which includes 208 videos. Experimental results on representative video clips demonstrate the efficacy of the proposed method. The dataset and our source code are available at https: //github. com/lovepiano/SMFN_For_360VSR.

Details DOI

AAAI Conference 2020 Conference Paper

An Annotation Sparsification Strategy for 3D Medical Image Segmentation via Representative Selection and Self-Training

Hao Zheng
Yizhe Zhang
Lin Yang
Chaoli Wang
Danny Z. Chen

Image segmentation is critical to lots of medical applications. While deep learning (DL) methods continue to improve performance for many medical image segmentation tasks, data annotation is a big bottleneck to DL-based segmentation because (1) DL models tend to need a large amount of labeled data to train, and (2) it is highly time-consuming and label-intensive to voxel-wise label 3D medical images. Signiﬁcantly reducing annotation effort while attaining good performance of DL segmentation models remains a major challenge. In our preliminary experiments, we observe that, using partially labeled datasets, there is indeed a large performance gap with respect to using fully annotated training datasets. In this paper, we propose a new DL framework for reducing annotation effort and bridging the gap between full annotation and sparse annotation in 3D medical image segmentation. We achieve this by (i) selecting representative slices in 3D images that minimize data redundancy and save annotation effort, and (ii) self-training with pseudo-labels automatically generated from the base-models trained using the selected annotated slices. Extensive experiments using two public datasets (the HVSMR 2016 Challenge dataset and mouse piriform cortex dataset) show that our framework yields competitive segmentation results comparing with state-of-the-art DL methods using less than ∼ 20% of annotated data.

PDF Details

AAAI Conference 2019 Conference Paper

A New Ensemble Learning Framework for 3D Biomedical Image Segmentation

Hao Zheng
Yizhe Zhang
Lin Yang
Peixian Liang
Zhuo Zhao
Chaoli Wang
Danny Z. Chen

3D image segmentation plays an important role in biomedical image analysis. Many 2D and 3D deep learning models have achieved state-of-the-art segmentation performance on 3D biomedical image datasets. Yet, 2D and 3D models have their own strengths and weaknesses, and by unifying them together, one may be able to achieve more accurate results. In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models. First, we develop a fully convolutional network based meta-learner to learn how to improve the results from 2D and 3D models (base-learners). Then, to minimize over-fitting for our sophisticated meta-learner, we devise a new training method that uses the results of the baselearners as multiple versions of “ground truths”. Furthermore, since our new meta-learner training scheme does not depend on manual annotation, it can utilize abundant unlabeled 3D image data to further improve the model. Extensive experiments on two public datasets (the HVSMR 2016 Challenge dataset and the mouse piriform cortex dataset) show that our approach is effective under fully-supervised, semisupervised, and transductive settings, and attains superior performance over state-of-the-art image segmentation methods.

PDF Details

AAAI Conference 2019 Conference Paper

Biomedical Image Segmentation via Representative Annotation

Hao Zheng
Lin Yang
Jianxu Chen
Jun Han
Yizhe Zhang
Peixian Liang
Zhuo Zhao
Chaoli Wang

Deep learning has been applied successfully to many biomedical image segmentation tasks. However, due to the diversity and complexity of biomedical image data, manual annotation for training common deep learning models is very timeconsuming and labor-intensive, especially because normally only biomedical experts can annotate image data well. Human experts are often involved in a long and iterative process of annotation, as in active learning type annotation schemes. In this paper, we propose representative annotation (RA), a new deep learning framework for reducing annotation effort in biomedical image segmentation. RA uses unsupervised networks for feature extraction and selects representative image patches for annotation in the latent space of learned feature descriptors, which implicitly characterizes the underlying data while minimizing redundancy. A fully convolutional network (FCN) is then trained using the annotated selected image patches for image segmentation. Our RA scheme offers three compelling advantages: (1) It leverages the ability of deep neural networks to learn better representations of image data; (2) it performs one-shot selection for manual annotation and frees annotators from the iterative process of common active learning based annotation schemes; (3) it can be deployed to 3D images with simple extensions. We evaluate our RA approach using three datasets (two 2D and one 3D) and show our framework yields competitive segmentation results comparing with state-of-the-art methods.

PDF Details