1 code implementation • 27 Sep 2022 • Haoning Lin, Changhao Sun, Yunpeng Liu
Trying to address these problems, this paper proposes OBBStacking, an ensemble method that is compatible with OBBs and combines the detection results in a learned fashion.
no code implementations • 7 Jun 2022 • HaoYuan Chen, Chen Li, Xiaoyan Li, Md Mamunur Rahaman, Weiming Hu, Yixin Li, Wanli Liu, Changhao Sun, Hongzan Sun, Xinyu Huang, Marcin Grzegorzek
In addition, we conducted an ablation experiment and an interchangeability experiment to verify the ability and interchangeability of the three channels.
no code implementations • 2 Jun 2022 • Wanli Liu, Chen Li, Ning Xu, Tao Jiang, Md Mamunur Rahaman, Hongzan Sun, Xiangchen Wu, Weiming Hu, HaoYuan Chen, Changhao Sun, YuDong Yao, Marcin Grzegorzek
Cervical cytopathology image classification is an important method to diagnose cervical cancer.
1 code implementation • 4 Jun 2021 • Weiming Hu, Chen Li, Xiaoyan Li, Md Mamunur Rahaman, Jiquan Ma, Yong Zhang, HaoYuan Chen, Wanli Liu, Changhao Sun, YuDong Yao, Hongzan Sun, Marcin Grzegorzek
In order to prove that the methods of different periods in the field of image classification have discrepancies on GasHisSDB, we select a variety of classifiers for evaluation.
no code implementations • 16 May 2021 • Wanli Liu, Chen Li, Md Mamunur Rahamana, Tao Jiang, Hongzan Sun, Xiangchen Wu, Weiming Hu, HaoYuan Chen, Changhao Sun, YuDong Yao, Marcin Grzegorzek
The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images.
no code implementations • 29 Apr 2021 • HaoYuan Chen, Chen Li, Ge Wang, Xiaoyan Li, Md Rahaman, Hongzan Sun, Weiming Hu, Yixin Li, Wanli Liu, Changhao Sun, Shiliang Ai, Marcin Grzegorzek
In this paper, a multi-scale visual transformer model, referred as GasHis-Transformer, is proposed for Gastric Histopathological Image Detection (GHID), which enables the automatic global detection of gastric cancer images.
no code implementations • 21 Feb 2021 • Yixin Li, Xinran Wu, Chen Li, Changhao Sun, Md Rahaman, HaoYuan Chen, YuDong Yao, Xiaoyan Li, Yong Zhang, Tao Jiang
The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module.
no code implementations • 6 Jan 2021 • Yuehong Gao, Changhao Sun, Xiaonan Zhang, Xiao Hong
Theoretical results in the scenario of a 5G New Radio system are presented, where the SNR thresholds for adaptive modulation and coding scheme selection, transmission rate and delay, as well as admission region under different configurations are discussed.
no code implementations • 29 Sep 2020 • Yixin Li, Chen Li, Xiaoyan Li, Kai Wang, Md Mamunur Rahaman, Changhao Sun, Hao Chen, Xinran Wu, Hong Zhang, Qian Wang
In this review, we present a comprehensive overview of pathology image analysis based on the markov random fields (MRFs) and conditional random fields (CRFs), which are two popular random field models.
no code implementations • 27 Mar 2020 • Xiaomin Zhou, Chen Li, Md Mamunur Rahaman, Yu-Dong Yao, Shiliang Ai, Changhao Sun, Xiaoyan Li, Qian Wang, Tao Jiang
Breast cancer is one of the most common and deadliest cancers among women.
no code implementations • 8 Mar 2020 • Jinghua Zhang, Chen Li, Frank Kulwa, Xin Zhao, Changhao Sun, Zihan Li, Tao Jiang, Hong Li
In order to assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multi-scale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper.
no code implementations • 3 Mar 2020 • Changhao Sun, Chen Li, Jinghua Zhang, Muhammad Rahaman, Shiliang Ai, Hao Chen, Frank Kulwa, Yixin Li, Xiaoyan Li, Tao Jiang
This HCRF model is built up with higher order potentials, including pixel-level and patch-level potentials, and graph-based post-processing is applied to further improve its segmentation performance.
no code implementations • 5 Dec 2018 • Haipeng Jia, Xueshuang Xiang, Da Fan, Meiyu Huang, Changhao Sun, Yang He
Addressing these two issues, this paper proposes the Drop Pruning approach, which leverages stochastic optimization in the pruning process by introducing a drop strategy at each pruning step, namely, drop away, which stochastically deletes some unimportant weights, and drop back, which stochastically recovers some pruned weights.