no code implementations • 4 Dec 2023 • Yitao Peng, Lianghua He, Die Hu, Yihang Liu, Longzhen Yang, Shaohua Shang
Due to the unique multi-instance learning of medical images and the difficulty in identifying decision-making regions, many interpretability models that have been proposed still have problems of insufficient accuracy and interpretability in medical image disease diagnosis.
no code implementations • 4 Mar 2021 • Wei zhang, Yi Jiang, Bin Zhou, Die Hu
This paper proposes a novel scheme for mitigating strong interferences, which is applicable to various wireless scenarios, including full-duplex wireless communications and uncoordinated heterogenous networks.
1 code implementation • 26 May 2019 • Guanyu Cai, Lianghua He, Mengchu Zhou, Hesham Alhumade, Die Hu
When constructing a deep end-to-end model, to ensure the effectiveness and stability of unsupervised domain adaptation, three critical factors are considered in our proposed optimization strategy, i. e., the sample amount of a target domain, dimension and batchsize of samples.
Ranked #1 on Domain Adaptation on SVNH-to-MNIST