Hung, Alex Ling Yu Zheng, Haoxin Miao, Qi Raman, Steven S Terzopoulos, Demetri Sung, Kyunghyun
Published in
IEEE transactions on medical imaging
Prostate cancer is the second leading cause of cancer death among men in the United States. The diagnosis of prostate MRI often relies on accurate prostate zonal segmentation. However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate M...
Najam, Asadullah
Recurrent Neural Networks (RNNs) are pivotal in deep learning for time series prediction, but they suffer from 'exploding values' and 'gradient decay,' particularly when learning temporally distant interactions. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) have addressed these issues to an extent, but the precise mitigating mechani...
Zhang, Ruoyi Ma, Huifang Li, Qingfeng Wang, Yike Li, Zhixin
Published in
Applied intelligence (Dordrecht, Netherlands)
To solve the information overload issue and enhance the user experience of various web applications, recommender systems aim to better model user interests and preferences. Knowledge Graphs (KGs), consisting of real-world objective facts and fruitful entities, play a vital role in recommender systems. Recently, a technological trend has been to dev...
Chen, Xu Kuang, Tianshu Deng, Hannah Fung, Steve H. Gateno, Jaime Xia, James J. Yap, Pew-Thian
Published in
IEEE transactions on medical imaging
Domain adaptation techniques have been demonstrated to be effective in addressing label deficiency challenges in medical image segmentation. However, conventional domain adaptation based approaches often concentrate on matching global marginal distributions between different domains in a class-agnostic fashion. In this paper, we present a dual-atte...
Deng, Ruining Cui, Can Remedios, Lucas W. Bao, Shunxing Womick, R. Michael Chiron, Sophie Li, Jia Roland, Joseph T. Lau, Ken S. Liu, Qi
...
Published in
Multiscale multimodal medical imaging : Third International Workshop, MMMI 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings
Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotations. Often, this approach directly applies “natural image driven” MIL algorithms which overlook the multi-scale (i.e. pyramidal) nature of WSIs. Off-the-shelf MIL algorithm...
Chen, Yu-Wen Li, Yu-Jie Deng, Peng Yang, Zhi-Yong Zhong, Kun-Hua Zhang, Li-Ge Chen, Yang Zhi, Hong-Yu Hu, Xiao-Yan Gu, Jian-Teng
...
Published in
BMC anesthesiology
Dynamic prediction of patient mortality risk in the ICU with time series data is limited due to high dimensionality, uncertainty in sampling intervals, and other issues. A new deep learning method, temporal convolution network (TCN), makes it possible to deal with complex clinical time series data in ICU. We aimed to develop and validate it to pred...
Liu, Yulin Li, Jiaolong Liu, Chuang Wei, Jiangshu
Published in
PeerJ. Computer science
Cultivated land quality is related to the quality and safety of agricultural products and to ecological safety. Therefore, reasonably evaluating the quality of land, which is helpful in identifying its benefits, is crucial. However, most studies have used traditional methods to estimate cultivated land quality, and there is little research on using...
Makarov, Ilya Bakhanova, Maria Nikolenko, Sergey Gerasimova, Olga
Published in
PeerJ. Computer science
Depth estimation has been an essential task for many computer vision applications, especially in autonomous driving, where safety is paramount. Depth can be estimated not only with traditional supervised learning but also via a self-supervised approach that relies on camera motion and does not require ground truth depth maps. Recently, major improv...
Duc Dao, Cuong
DEtection TRansformer, DETR, introduces an innovative design for object detection based on softmax attention. However, the softmax operation produces dense attention patterns, i.e., all entries in the attention matrix receive a non-zero weight, regardless of their relevance for detection. In this work, we explore several alternatives to softmax to ...
Zhu, Shuangling Mujiang, Guli Nazi·Aili Jumahong, Huxidan Maiti, Pazi Laiti·Nuer
Published in
Journal of Physics: Conference Series
A U-Net convolutional network structure is fully capable of completing the end-to-end training with extremely little data, and can achieve better results. When the convolutional network has a short link between a near input layer and a near output layer, it can implement training in a deeper, more accurate and effective way. This paper mainly propo...
