Search Results for author: Qianni Zhang

Found 26 papers, 14 papers with code

DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Indistinct-Boundary Object Segmentation

2 code implementations1 Nov 2023 Xiaohua Jiang, Yihao Guo, Jian Huang, Yuting Wu, Meiyi Luo, Zhaoyang Xu, Qianni Zhang, Xingru Huang, Hong He, Shaowei Jiang, Jing Ye, Mang Xiao

The precise spatial and quantitative delineation of indistinct-boundary medical objects is paramount for the accuracy of diagnostic protocols, efficacy of surgical interventions, and reliability of postoperative assessments.

3D Reconstruction Data Augmentation +3

Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic Segmentation

1 code implementation ICCV 2023 Zongyi Xu, Bo Yuan, Shanshan Zhao, Qianni Zhang, Xinbo Gao

The most recent methods of this kind measure the uncertainty of each pre-divided region for manual labelling but they suffer from redundant information and require additional efforts for region division.

Active Learning Point Cloud Segmentation +2

Joint Dense-Point Representation for Contour-Aware Graph Segmentation

1 code implementation21 Jun 2023 Kit Mills Bransby, Greg Slabaugh, Christos Bourantas, Qianni Zhang

We present a novel methodology that combines graph and dense segmentation techniques by jointly learning both point and pixel contour representations, thereby leveraging the benefits of each approach.

Segmentation

3D Coronary Vessel Reconstruction from Bi-Plane Angiography using Graph Convolutional Networks

no code implementations28 Feb 2023 Kit Mills Bransby, Vincenzo Tufaro, Murat Cap, Greg Slabaugh, Christos Bourantas, Qianni Zhang

X-ray coronary angiography (XCA) is used to assess coronary artery disease and provides valuable information on lesion morphology and severity.

3D Reconstruction UNET Segmentation

CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation

1 code implementation2 Aug 2022 Weiwei Cui, Yaqi Wang, Yilong Li, Dan Song, Xingyong Zuo, Jiaojiao Wang, Yifan Zhang, Huiyu Zhou, Bung san Chong, Liaoyuan Zeng, Qianni Zhang

This work provides a new benchmark for the tooth volume segmentation task, and the experiment can serve as the baseline for future AI-based dental imaging research and clinical application development.

Active Learning Segmentation

Complexity Reduction of Learned In-Loop Filtering in Video Coding

no code implementations16 Mar 2022 Woody Bayliss, Luka Murn, Ebroul Izquierdo, Qianni Zhang, Marta Mrak

In video coding, in-loop filters are applied on reconstructed video frames to enhance their perceptual quality, before storing the frames for output.

Dispensed Transformer Network for Unsupervised Domain Adaptation

no code implementations28 Oct 2021 Yunxiang Li, Jingxiong Li, Ruilong Dan, Shuai Wang, Kai Jin, Guodong Zeng, Jun Wang, Xiangji Pan, Qianni Zhang, Huiyu Zhou, Qun Jin, Li Wang, Yaqi Wang

To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper.

Unsupervised Domain Adaptation

GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation

1 code implementation30 Sep 2021 Yunxiang Li, Shuai Wang, Jun Wang, Guodong Zeng, Wenjun Liu, Qianni Zhang, Qun Jin, Yaqi Wang

In this paper, we propose a novel end-to-end U-Net like Group Transformer Network (GT U-Net) for the tooth root segmentation.

Anatomy Segmentation

Structure-aware scale-adaptive networks for cancer segmentation in whole-slide images

1 code implementation26 Sep 2021 Yibao Sun, Giussepi Lopez, Yaqi Wang, Xingru Huang, Huiyu Zhou, Qianni Zhang

Cancer segmentation in whole-slide images is a fundamental step for viable tumour burden estimation, which is of great value for cancer assessment.

Decoder feature selection +2

Magnification-independent Histopathological Image Classification with Similarity-based Multi-scale Embeddings

1 code implementation2 Jul 2021 Yibao Sun, Xingru Huang, Yaqi Wang, Huiyu Zhou, Qianni Zhang

Experimental results show that the SMSE improves the performance for histopathological image classification tasks for both breast and liver cancers by a large margin compared to previous methods.

Histopathological Image Classification Image Classification

SRPN: similarity-based region proposal networks for nuclei and cells detection in histology images

1 code implementation25 Jun 2021 Yibao Sun, Xingru Huang, Huiyu Zhou, Qianni Zhang

The embedding layer is added into the region proposal networks, enabling the networks to learn discriminative features based on similarity learning.

Cell Detection Dense Object Detection +2

AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapy

1 code implementation2 May 2021 Yunxiang Li, Guodong Zeng, Yifan Zhang, Jun Wang, Qianni Zhang, Qun Jin, Lingling Sun, Qisi Lian, Neng Xia, Ruizi Peng, Kai Tang, Yaqi Wang, Shuai Wang

Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in root canal therapy since the incorrect interpretation of the therapy results will hamper timely follow-up which is crucial to the patients' treatment outcome.

Anatomy General Classification

Multimodal Gait Recognition for Neurodegenerative Diseases

1 code implementation7 Jan 2021 Aite Zhao, Jianbo Li, Junyu Dong, Lin Qi, Qianni Zhang, Ning li, Xin Wang, Huiyu Zhou

In recent years, single modality based gait recognition has been extensively explored in the analysis of medical images or other sensory data, and it is recognised that each of the established approaches has different strengths and weaknesses.

Gait Recognition

Perceptual underwater image enhancement with deep learning and physical priors

no code implementations21 Aug 2020 Long Chen, Zheheng Jiang, Lei Tong, Zhihua Liu, Aite Zhao, Qianni Zhang, Junyu Dong, Huiyu Zhou

Underwater image enhancement, as a pre-processing step to improve the accuracy of the following object detection task, has drawn considerable attention in the field of underwater navigation and ocean exploration.

Image Enhancement Image Generation +2

CANet: Context Aware Network for 3D Brain Glioma Segmentation

1 code implementation15 Jul 2020 Zhihua Liu, Lei Tong, Long Chen, Feixiang Zhou, Zheheng Jiang, Qianni Zhang, Yinhai Wang, Caifeng Shan, Ling Li, Huiyu Zhou

Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning.

Brain Tumor Segmentation Segmentation +1

US-net for robust and efficient nuclei instance segmentation

no code implementations31 Jan 2019 Zhaoyang Xu, Faranak Sobhani, Carlos Fernandez Moro, Qianni Zhang

We present a novel neural network architecture, US-Net, for robust nuclei instance segmentation in histopathology images.

Instance Segmentation Segmentation +1

GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis

no code implementations13 Jan 2019 Zhaoyang Xu, Xingru Huang, Carlos Fernández Moro, Béla Bozóky, Qianni Zhang

We proposed a conditional CycleGAN (cCGAN) network to transform the H\&E stained images into IHC stained images, facilitating virtual IHC staining on the same slide.

Translation

CUNet: A Compact Unsupervised Network for Image Classification

no code implementations6 Jul 2016 Le Dong, Ling He, Gaipeng Kong, Qianni Zhang, Xiaochun Cao, Ebroul Izquierdo

In this paper, we propose a compact network called CUNet (compact unsupervised network) to counter the image classification challenge.

Classification General Classification +1

NIST: An Image Classification Network to Image Semantic Retrieval

no code implementations2 Jul 2016 Le Dong, Xiuyuan Chen, Mengdie Mao, Qianni Zhang

This paper proposes a classification network to image semantic retrieval (NIST) framework to counter the image retrieval challenge.

Classification General Classification +4

A Distributed Deep Representation Learning Model for Big Image Data Classification

no code implementations2 Jul 2016 Le Dong, Na Lv, Qianni Zhang, Shanshan Xie, Ling He, Mengdie Mao

The result implies that our approach is more efficient than the conventional deep learning approaches, and can be applied to big data that is too complex for parameter designing focused approaches.

Distributed Computing General Classification +2

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