Search Results for author: Jiacheng Wang

Found 15 papers, 10 papers with code

Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services

no code implementations31 Jul 2022 Hongyang Du, Jiacheng Wang, Dusit Niyato, Jiawen Kang, Zehui Xiong, Xuemin, Shen, Dong In Kim

With the help of UOAL, we propose an attention-aware network resource allocation algorithm that has two steps, i. e., attention prediction and QoE maximization.

Personalizing Federated Medical Image Segmentation via Local Calibration

1 code implementation11 Jul 2022 Jiacheng Wang, Yueming Jin, Liansheng Wang

Personalized FL tackles this issue by only utilizing partial model parameters shared from global server, while keeping the rest to adapt to its own data distribution in the local training of each site.

Federated Learning Medical Image Segmentation +1

XBound-Former: Toward Cross-scale Boundary Modeling in Transformers

1 code implementation2 Jun 2022 Jiacheng Wang, Fei Chen, Yuxi Ma, Liansheng Wang, Zhaodong Fei, Jianwei Shuai, Xiangdong Tang, Qichao Zhou, Jing Qin

Skin lesion segmentation from dermoscopy images is of great significance in the quantitative analysis of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i. e., considerable size, shape and color variation, and ambiguous boundaries.

Lesion Segmentation Skin Lesion Segmentation

ModDrop++: A Dynamic Filter Network with Intra-subject Co-training for Multiple Sclerosis Lesion Segmentation with Missing Modalities

1 code implementation7 Mar 2022 Han Liu, Yubo Fan, Hao Li, Jiacheng Wang, Dewei Hu, Can Cui, Ho Hin Lee, Huahong Zhang, Ipek Oguz

Previously, a training strategy termed Modality Dropout (ModDrop) has been applied to MS lesion segmentation to achieve the state-of-the-art performance with missing modality.

Lesion Segmentation

Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network

1 code implementation8 Nov 2021 Jiacheng Wang, Yueming Jin, Shuntian Cai, Hongzhi Xu, Pheng-Ann Heng, Jing Qin, Liansheng Wang

Compared with existing solutions, which either neglect geometric relationships among targeting objects or capture the relationships by using complicated aggregation schemes, the proposed network is capable of achieving satisfactory accuracy while maintaining real-time performance by taking full advantage of the spatial relations among landmarks.

Multi-Task Learning

Boundary-aware Transformers for Skin Lesion Segmentation

1 code implementation8 Oct 2021 Jiacheng Wang, Lan Wei, Liansheng Wang, Qichao Zhou, Lei Zhu, Jing Qin

Skin lesion segmentation from dermoscopy images is of great importance for improving the quantitative analysis of skin cancer.

Inductive Bias Lesion Segmentation +1

Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video

1 code implementation28 Sep 2021 Jiacheng Wang, Yueming Jin, Liansheng Wang, Shuntian Cai, Pheng-Ann Heng, Jing Qin

On the other hand, we develop an active global memory to gather the global semantic correlation in long temporal range to current one, in which we gather the most informative frames derived from model uncertainty and frame similarity.

Optical Flow Estimation

Self-semi-supervised Learning to Learn from NoisyLabeled Data

no code implementations3 Nov 2020 Jiacheng Wang, Yue Ma, Shuang Gao

The remarkable success of today's deep neural networks highly depends on a massive number of correctly labeled data.

Meta-Learning for Natural Language Understanding under Continual Learning Framework

1 code implementation3 Nov 2020 Jiacheng Wang, Yong Fan, Duo Jiang, Shiqing Li

Neural network has been recognized with its accomplishments on tackling various natural language understanding (NLU) tasks.

Continual Learning Meta-Learning +1

Neyman-Pearson classification: parametrics and sample size requirement

no code implementations7 Feb 2018 Xin Tong, Lucy Xia, Jiacheng Wang, Yang Feng

In this work, we employ the parametric linear discriminant analysis (LDA) model and propose a new parametric thresholding algorithm, which does not need the minimum sample size requirements on class $0$ observations and thus is suitable for small sample applications such as rare disease diagnosis.

Classification General Classification +1

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