Now showing items 1-4 of 4

    • Adaptive Proxy Anchor Loss for Deep Metric Learning 

      Nguyen, Do Trung Chanh; Nguyen, Phan; Tran, Sen; Ta, Duc Huy; Duong, T.M. Soan; Bui, Trung; Truong, Q.H. Steven; Pham, Hong Thinh; Nguyen, Thanh Dat; Nguyen, Huu Thanh; Nguyen, M. Hien; Truong, Thu Huong (2022-10)
      Deep metric learning (or simply called metric learning) uses the deep neural network to learn the representation of images, leading to widely used in many applications, e.g. image retrieval and face recognition. In the ...
    • Benchmarking saliency methods for chest X-ray interpretation 

      Nguyen, Do Trung Chanh; Saporta, Adriel; Agrawal, Ashwin; Pareek, Anuj; Truong, Steven Q. H.; Ngo, Van Doan; Seekins, Jayne; Blankenberg, Francis G.; Ng, Andrew Y.; Lungren, Matthew P.; Rajpurkar, Pranav (2022-08)
      Saliency methods, which produce heat maps that highlight the areas of the medical image that influence model prediction, are often presented to clinicians as an aid in diagnostic decision-making. However, rigorous investigation ...
    • CapNext: Unifying Capsule and ResNeXt for Medical Image Segmentation 

      Nguyen, Do Trung Chanh; Huynh, Thanh M.; Nguyen, Khoa N. A.; Truong, Steven Q. H.; Bui, Trung (2022)
      Capsule Network is a contemporary approach to image analysis that emphasizes part-whole relationships. However, its applications to segmentation tasks are limited due to training difficulties such as initialization and ...
    • SEGTRANSVAE: Hybrid CNN-Transformer with Regularization for Medical Image Segmentation 

      Nguyen, Do Trung Chanh; Pham, Quan Dung; Nguyen, Truong Hai; Nguyen, Phuong Nam; Nguyen, Khoa N. A.; Bui, Trung; Truong, Steven Q.H. (2022)
      Current research on deep learning for medical image segmentation highlights limitations in learning either global semantic information or local contextual information effectively. To address these challenges, this paper ...

      Vin University Library
      Da Ton, Gia Lam
      Vinhomes Oceanpark, Ha Noi, Viet Nam
      Phone: +84-2471-089-779 | 1800-8189
      Contact: library@vinuni.edu.vn