Blar i forfatter "Dong, Nanqing"

Viser treff 1-6 av 6

    • ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation 

      Kampffmeyer, Michael C.; Dong, Nanqing; Liang, Xiaodan; Zhang, Yujia; Xing, Eric P. (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-12-14)
      Salient segmentation aims to segment out attention-grabbing regions, a critical yet challenging task and the foundation of many high-level computer vision applications. It requires semantic-aware grouping of pixels into salient regions and benefits from the utilization of global multi-scale contexts to achieve good local reasoning. Previous works often address it as two-class segmentation problems ...

    • Federated Partially Supervised Learning With Limited Decentralized Medical Images 

      Dong, Nanqing; Kampffmeyer, Michael; Voiculescu, Irina; Xing, Eric (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-12-20)
      Data government has played an instrumental role in securing the privacy-critical infrastructure in the medical domain and has led to an increased need of federated learning (FL). While decentralization can limit the effectiveness of standard supervised learning, the impact of decentralization on partially supervised learning remains unclear. Besides, due to data scarcity, each client may have access ...

    • Negational symmetry of quantum neural networks for binary pattern classification 

      Dong, Nanqing; Kampffmeyer, Michael; Voiculescu, Irina; Xing, Eric (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-04-27)
      Although quantum neural networks (QNNs) have shown promising results in solving simple machine learning tasks recently, the behavior of QNNs in binary pattern classification is still underexplored. In this work, we find that QNNs have an Achilles’ heel in binary pattern classification. To illustrate this point, we provide a theoretical insight into the properties of QNNs by presenting and analyzing ...

    • Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-Slide Images 

      Dong, Nanqing; Kampffmeyer, Michael C.; Liang, Xiaodan; Wang, Zeya; Dai, Wei; Xing, Eric P. (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-09-20)
      Convolutional neural networks have led to significant breakthroughs in the domain of medical image analysis. However, the task of breast cancer segmentation in whole-slide images (WSIs) is still underexplored. WSIs are large histopathological images with extremely high resolution. Constrained by the hardware and field of view, using high-magnification patches can slow down the inference process and ...

    • Towards robust partially supervised multi-structure medical image segmentation on small-scale data 

      Dong, Nanqing; Kampffmeyer, Michael; Liang, Xiaodan; Xu, Min; Voiculescu, Irina; Xing, Eric (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-11-20)
      The data-driven nature of deep learning (DL) models for semantic segmentation requires a large number of pixel-level annotations. However, large-scale and fully labeled medical datasets are often unavailable for practical tasks. Recently, partially supervised methods have been proposed to utilize images with incomplete labels in the medical domain. To bridge the methodological gaps in ...

    • Unsupervised domain adaptation for automatic estimation of cardiothoracic ratio 

      Dong, Nanqing; Kampffmeyer, Michael C.; Liang, Xiaodan; Wang, Zeya; Dai, Wei; Xing, Eric P. (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-09-26)
      The cardiothoracic ratio (CTR), a clinical metric of heart size in chest X-rays (CXRs), is a key indicator of cardiomegaly. Manual measurement of CTR is time-consuming and can be affected by human subjectivity, making it desirable to design computer-aided systems that assist clinicians in the diagnosis process. Automatic CTR estimation through chest organ segmentation, however, requires large amounts ...