Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring.
Diabetic retinopathy (DR) is one of the most common eye conditions among diabetic patients.
Deep learning has achieved promising segmentation performance on 3D left atrium MR images.
An AutoML method based on XGBoost, termed AutoGBM, is built as the classifier for prediction and feature ranking.
We use the proposed criteria to select samples for strong and weak labelers to produce oracle labels and pseudo labels simultaneously at each active learning iteration in an ensemble learning manner, which can be examined with IoMT Platform.
The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation.
We model the DL-PBS system from the perspective of CPS and use deep learning to predict the layout of bicycle parking spots and the dynamic demand of bicycle dispatching.
Secondly, we propose balanced Silhouette Index (bSI) to evaluate the internal quality of imbalanced clustering.
Specifically, CNN is utilized to model the spatial relations and the short-term temporal dependencies among sensors, while the output features of CNN are fed into the GRU to learn the long-term temporal dependencies jointly.
no code implementations • 8 May 2020 • Jiapan Gu, Ziyuan Zhao, Zeng Zeng, Yuzhe Wang, Zhengyiren Qiu, Bharadwaj Veeravalli, Brian Kim Poh Goh, Glenn Kunnath Bonney, Krishnakumar Madhavan, Chan Wan Ying, Lim Kheng Choon, Thng Choon Hua, Pierce KH Chow
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the fourth most common cause of cancer-related death worldwide.
Gene mutation prediction in hepatocellular carcinoma (HCC) is of great diagnostic and prognostic value for personalized treatments and precision medicine.
Water quality has a direct impact on industry, agriculture, and public health.
Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective to learn temporal dependencies than what we expected and implicitly yields an orderless representation.
no code implementations • 9 Nov 2019 • Ramanpreet Singh Pahwa, Jin Chao, Jestine Paul, Yiqun Li, Ma Tin Lay Nwe, Shudong Xie, Ashish James, ArulMurugan Ambikapathi, Zeng Zeng, Vijay Ramaseshan Chandrasekhar
In this paper, a multi-phase deep learning based technique is proposed to perform accurate fault detection of rail-valves.
In this paper, we present an annotated cribriform dataset along with analysis of deep learning models and hand-crafted features for cribriform pattern detection in prostate histopathological images.
Nonlinear regression has been extensively employed in many computer vision problems (e. g., crowd counting, age estimation, affective computing).
In this study, we applied powerful deep neural network and explored a process in the forecast of skeletal bone age with the specifically combine joints images to increase the performance accuracy compared with the whole hand images.
A fast and accurate predictive tool for polymer properties is demanding and will pave the way to iterative inverse design.
In particular, we unify traditional "knowledgeless" machine learning models and knowledge graphs in a novel end-to-end framework.
1 code implementation • 17 Jun 2017 • Zhe Wang, Kingsley Kuan, Mathieu Ravaut, Gaurav Manek, Sibo Song, Yuan Fang, Seokhwan Kim, Nancy Chen, Luis Fernando D'Haro, Luu Anh Tuan, Hongyuan Zhu, Zeng Zeng, Ngai Man Cheung, Georgios Piliouras, Jie Lin, Vijay Chandrasekhar
Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text.
We present a deep learning framework for computer-aided lung cancer diagnosis.
In this study, a multi-task deep neural network is proposed for skin lesion analysis.