1 code implementation • 26 Mar 2024 • Han Yuan, Chuan Hong, PengTao Jiang, Gangming Zhao, Nguyen Tuan Anh Tran, Xinxing Xu, Yet Yen Yan, Nan Liu
We anticipate that our template guidance will forge a fresh approach to elucidating AI models by integrating clinical domain expertise.
1 code implementation • 8 Mar 2024 • Siqi Li, Yuqing Shang, Ziwen Wang, Qiming Wu, Chuan Hong, Yilin Ning, Di Miao, Marcus Eng Hock Ong, Bibhas Chakraborty, Nan Liu
We applied our approach to sites with heterogeneous survival data originating from emergency departments in Singapore and the United States.
no code implementations • 8 Mar 2024 • Mingxuan Liu, Yilin Ning, Yuhe Ke, Yuqing Shang, Bibhas Chakraborty, Marcus Eng Hock Ong, Roger Vaughan, Nan Liu
The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness.
no code implementations • 4 Mar 2024 • Ziwen Wang, Jin Wee Lee, Tanujit Chakraborty, Yilin Ning, Mingxuan Liu, Feng Xie, Marcus Eng Hock Ong, Nan Liu
The calibration of DeepSurv (IBS: 0. 041) performed the best, followed by RSF (IBS: 0. 042) and GBM (IBS: 0. 0421), all using the full variables.
no code implementations • 15 Feb 2024 • Ting Fang Tan, Kabilan Elangovan, Liyuan Jin, Yao Jie, Li Yong, Joshua Lim, Stanley Poh, Wei Yan Ng, Daniel Lim, Yuhe Ke, Nan Liu, Daniel Shu Wei Ting
200 responses to the testing dataset were generated by 5 fine-tuned LLMs for evaluation.
no code implementations • 29 Jan 2024 • Yuhe Ke, Liyuan Jin, Kabilan Elangovan, Hairil Rizal Abdullah, Nan Liu, Alex Tiong Heng Sia, Chai Rick Soh, Joshua Yi Min Tung, Jasmine Chiat Ling Ong, Daniel Shu Wei Ting
Compared to the human-generated instructions, which had an accuracy of 86. 3%, the performance of the GPT4. 0 RAG model demonstrated non-inferiority (p=0. 610).
no code implementations • 26 Jan 2024 • Yu He Ke, Rui Yang, Sui An Lie, Taylor Xin Yi Lim, Hairil Rizal Abdullah, Daniel Shu Wei Ting, Nan Liu
Results: In a total of 80 responses evaluating both initial and final diagnoses, the initial diagnosis had an accuracy of 0% (0/80), but following multi-agent discussions, the accuracy for the top differential diagnosis increased to 71. 3% (57/80), and for the final two differential diagnoses, to 80. 0% (64/80).
no code implementations • 16 Dec 2023 • Jinyong Hahn, Zhipeng Liao, Nan Liu, Shuyang Sheng
This paper shows that the endogeneity test using the control function approach in linear instrumental variable models is a variant of the Hausman test.
no code implementations • 26 Nov 2023 • Han Yuan, Chuan Hong, Nguyen Tuan Anh Tran, Xinxing Xu, Nan Liu
We propose a novel approach that incorporates the lung+ space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs.
1 code implementation • 6 Nov 2023 • Siqi Li, Di Miao, Qiming Wu, Chuan Hong, Danny D'Agostino, Xin Li, Yilin Ning, Yuqing Shang, Huazhu Fu, Marcus Eng Hock Ong, Hamed Haddadi, Nan Liu
Our goal was to bridge the gap by presenting the first comprehensive comparison of FL frameworks from both engineering and statistical domains.
2 code implementations • 2 Nov 2023 • Yilin Ning, Salinelat Teixayavong, Yuqing Shang, Julian Savulescu, Vaishaanth Nagaraj, Di Miao, Mayli Mertens, Daniel Shu Wei Ting, Jasmine Chiat Ling Ong, Mingxuan Liu, Jiuwen Cao, Michael Dunn, Roger Vaughan, Marcus Eng Hock Ong, Joseph Jao-Yiu Sung, Eric J Topol, Nan Liu
The widespread use of ChatGPT and other emerging technology powered by generative artificial intelligence (GenAI) has drawn much attention to potential ethical issues, especially in high-stakes applications such as healthcare, but ethical discussions are yet to translate into operationalisable solutions.
no code implementations • 30 Oct 2023 • Sizhe Li, Xun Ma, Nan Liu, Yi Jin
Transmission line state assessment and prediction are of great significance for the rational formulation of operation and maintenance strategy and improvement of operation and maintenance level.
no code implementations • ICCV 2023 • Nan Liu, Yilun Du, Shuang Li, Joshua B. Tenenbaum, Antonio Torralba
Text-to-image generative models have enabled high-resolution image synthesis across different domains, but require users to specify the content they wish to generate.
no code implementations • 26 Apr 2023 • Mingxuan Liu, Yilin Ning, Salinelat Teixayavong, Mayli Mertens, Jie Xu, Daniel Shu Wei Ting, Lionel Tim-Ee Cheng, Jasmine Chiat Ling Ong, Zhen Ling Teo, Ting Fang Tan, Ravi Chandran Narrendar, Fei Wang, Leo Anthony Celi, Marcus Eng Hock Ong, Nan Liu
In this paper, we discuss the misalignment between technical and clinical perspectives of AI fairness, highlight the barriers to AI fairness' translation to healthcare, advocate multidisciplinary collaboration to bridge the knowledge gap, and provide possible solutions to address the clinical concerns pertaining to AI fairness.
no code implementations • 14 Apr 2023 • Siqi Li, Pinyan Liu, Gustavo G. Nascimento, Xinru Wang, Fabio Renato Manzolli Leite, Bibhas Chakraborty, Chuan Hong, Yilin Ning, Feng Xie, Zhen Ling Teo, Daniel Shu Wei Ting, Hamed Haddadi, Marcus Eng Hock Ong, Marco Aurélio Peres, Nan Liu
Structured data, one of the most prevalent forms of clinical data, has experienced significant growth in volume concurrently, notably with the widespread adoption of electronic health records in clinical practice.
no code implementations • 7 Apr 2023 • Yilin Ning, Victor Volovici, Marcus Eng Hock Ong, Benjamin Alan Goldstein, Nan Liu
A prediction model is most useful if it generalizes beyond the development data with external validations, but to what extent should it generalize remains unclear.
1 code implementation • 1 Mar 2023 • Siqi Li, Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty, Chuan Hong, Feng Xie, Han Yuan, Mingxuan Liu, Daniel M. Buckland, Yong Chen, Nan Liu
We also calculated the average AUC values and SDs for each local model, and the FedScore model showed promising accuracy and stability with a high average AUC value which was closest to the one of the pooled model and SD which was lower than that of most local models.
no code implementations • 16 Dec 2022 • Yilin Ning, Mingxuan Liu, Nan Liu
Current practice in interpretable machine learning often focuses on explaining the final model trained from data, e. g., by using the Shapley additive explanations (SHAP) method.
1 code implementation • 16 Dec 2022 • Madhurima Panja, Tanujit Chakraborty, Sk Shahid Nadim, Indrajit Ghosh, Uttam Kumar, Nan Liu
In comparison with statistical, machine learning, and deep learning methods, our proposed XEWNet performs better in 75% of the cases for short-term and long-term forecasting of dengue incidence.
no code implementations • 15 Nov 2022 • Jin Li, Gui Zhou, Tantao Gong, Nan Liu
For the case of a single communication user, we consider three types of echo interference, no interference, a point interference, and an extended interference.
no code implementations • 8 Nov 2022 • Jin Li, Nan Liu
A closed-form solution with low complexity and a solution based on the semidefinite relaxation (SDR) method are provided to solve these two problems, respectively.
no code implementations • ECCV 2022 • Jingyuan Ma, Xiangyu Lei, Nan Liu, Xian Zhao, ShiLiang Pu
Semantics-guided self-supervised monocular depth estimation has been widely researched, owing to the strong cross-task correlation of depth and semantics.
no code implementations • 15 Oct 2022 • Mingxuan Liu, Siqi Li, Han Yuan, Marcus Eng Hock Ong, Yilin Ning, Feng Xie, Seyed Ehsan Saffari, Victor Volovici, Bibhas Chakraborty, Nan Liu
We found that model backbone(s) differed among data types as well as the imputation strategy.
1 code implementation • 21 Jun 2022 • Madhurima Panja, Tanujit Chakraborty, Uttam Kumar, Nan Liu
Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics.
1 code implementation • 8 Jun 2022 • Mingxuan Liu, Yilin Ning, Han Yuan, Marcus Eng Hock Ong, Nan Liu
This study sought to investigate the effects of data imbalance on SHAP explanations for deep learning models, and to propose a strategy to mitigate these effects.
no code implementations • 6 Jun 2022 • Yuzhe Li, Yong liu, Bo Li, Weiping Wang, Nan Liu
In this paper, we focus our attention on private Empirical Risk Minimization (ERM), which is one of the most commonly used data analysis method.
1 code implementation • 3 Jun 2022 • Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, Joshua B. Tenenbaum
Large text-guided diffusion models, such as DALLE-2, are able to generate stunning photorealistic images given natural language descriptions.
1 code implementation • 17 Feb 2022 • Seyed Ehsan Saffari, Yilin Ning, Xie Feng, Bibhas Chakraborty, Victor Volovici, Roger Vaughan, Marcus Eng Hock Ong, Nan Liu
This study aims to expand the AutoScore framework to provide a tool for interpretable risk prediction for ordinal outcomes.
1 code implementation • 10 Jan 2022 • Yilin Ning, Siqi Li, Marcus Eng Hock Ong, Feng Xie, Bibhas Chakraborty, Daniel Shu Wei Ting, Nan Liu
Risk scores are widely used for clinical decision making and commonly generated from logistic regression models.
1 code implementation • 22 Nov 2021 • Feng Xie, Jun Zhou, Jin Wee Lee, Mingrui Tan, Siqi Li, Logasan S/O Rajnthern, Marcel Lucas Chee, Bibhas Chakraborty, An-Kwok Ian Wong, Alon Dagan, Marcus Eng Hock Ong, Fei Gao, Nan Liu
In this paper, based on the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) database, we developed a publicly available benchmark suite for ED triage predictive models and created a benchmark dataset that contains over 400, 000 ED visits from 2011 to 2019.
no code implementations • NeurIPS 2021 • Nan Liu, Shuang Li, Yilun Du, Joshua B. Tenenbaum, Antonio Torralba
The visual world around us can be described as a structured set of objects and their associated relations.
1 code implementation • 6 Oct 2021 • Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty, Benjamin Alan Goldstein, Daniel Shu Wei Ting, Roger Vaughan, Nan Liu
Interpretable machine learning has been focusing on explaining final models that optimize performance.
no code implementations • 21 Jul 2021 • Feng Xie, Han Yuan, Yilin Ning, Marcus Eng Hock Ong, Mengling Feng, Wynne Hsu, Bibhas Chakraborty, Nan Liu
To some extent, current deep learning solutions can address these challenges.
1 code implementation • 13 Jul 2021 • Han Yuan, Feng Xie, Marcus Eng Hock Ong, Yilin Ning, Marcel Lucas Chee, Seyed Ehsan Saffari, Hairil Rizal Abdullah, Benjamin Alan Goldstein, Bibhas Chakraborty, Nan Liu
All scoring models were evaluated on the basis of their area under the curve (AUC) in the receiver operating characteristic analysis and balanced accuracy (i. e., mean value of sensitivity and specificity).
1 code implementation • 13 Jun 2021 • Feng Xie, Yilin Ning, Han Yuan, Benjamin Alan Goldstein, Marcus Eng Hock Ong, Nan Liu, Bibhas Chakraborty
We illustrated our method in a real-life study of 90-day mortality of patients in intensive care units and compared its performance with survival models (i. e., Cox) and the random survival forest.
BIG-bench Machine Learning Interpretable Machine Learning +1
1 code implementation • ACL 2022 • Santiago Castro, Ruoyao Wang, Pingxuan Huang, Ian Stewart, Oana Ignat, Nan Liu, Jonathan C. Stroud, Rada Mihalcea
We propose fill-in-the-blanks as a video understanding evaluation framework and introduce FIBER -- a novel dataset consisting of 28, 000 videos and descriptions in support of this evaluation framework.
no code implementations • IEEE Network 2021 • Yuzheng Li, Chuan Chen, Nan Liu, Huawei Huang, Zibin Zheng, and Qiang Yan
To address these security issues, we propose a decentralized federated learning framework based on blockchain, that is, a Blockchain- based Federated Learning framework with Committee consensus (BFLC).
no code implementations • 1 Jan 2021 • Yuzhe Li, Yong liu, Weipinng Wang, Bo Li, Nan Liu
In this paper, we deduce the influence of $\epsilon$ on utility private learning models through strict mathematical derivation, and propose a novel approximate approach for estimating the utility of any $\epsilon$ value.
1 code implementation • 2 Apr 2020 • Yuzheng Li, Chuan Chen, Nan Liu, Huawei Huang, Zibin Zheng, Qiang Yan
To address these security issues, we proposed a decentralized federated learning framework based on blockchain, i. e., a Blockchain-based Federated Learning framework with Committee consensus (BFLC).
1 code implementation • WS 2019 • Yuzhong Hong, Xianguo Yu, Neng He, Nan Liu, Junhui Liu
We propose a Chinese spell checker {--} FASPell based on a new paradigm which consists of a denoising autoencoder (DAE) and a decoder.
no code implementations • 3 May 2019 • Wenmian Yang, Kun Wang, Na Ruan, Wenyuan Gao, Weijia Jia, Wei Zhao, Nan Liu, Yunyong Zhang
Finally, we gain the weight of each word by combining Semantic Weight (SW) and Inverse Document Frequency (IDF).
no code implementations • 14 Jun 2017 • Christian Rupprecht, Ansh Kapil, Nan Liu, Lamberto Ballan, Federico Tombari
One of the main problems in webly-supervised learning is cleaning the noisy labeled data from the web.