no code implementations • 27 Mar 2024 • Young Seok Jeon, Hongfei Yang, Huazhu Fu, Mengling Feng
Imposing key anatomical features, such as the number of organs, their shapes, sizes, and relative positions, is crucial for building a robust multi-organ segmentation model.
no code implementations • 19 Feb 2024 • Jihai Zhang, Xiang Lan, Xiaoye Qu, Yu Cheng, Mengling Feng, Bryan Hooi
Self-Supervised Contrastive Learning has proven effective in deriving high-quality representations from unlabeled data.
no code implementations • 26 Dec 2023 • Meng Ge, Yizhou Peng, Yidi Jiang, Jingru Lin, Junyi Ao, Mehmet Sinan Yildirim, Shuai Wang, Haizhou Li, Mengling Feng
This paper summarizes our team's efforts in both tracks of the ICMC-ASR Challenge for in-car multi-channel automatic speech recognition.
no code implementations • 8 Nov 2023 • Jingru Lin, Meng Ge, Wupeng Wang, Haizhou Li, Mengling Feng
Self-supervised pre-trained speech models were shown effective for various downstream speech processing tasks.
1 code implementation • 9 Oct 2023 • Kai He, Rui Mao, Qika Lin, Yucheng Ruan, Xiang Lan, Mengling Feng, Erik Cambria
This shift encompasses a move from discriminative AI approaches to generative AI approaches, as well as a shift from model-centered methodologies to datacentered methodologies.
no code implementations • 9 Oct 2023 • Ling Huang, Su Ruan, Yucheng Xing, Mengling Feng
Generally, this review aims to allow researchers from both clinical and technical backgrounds to gain a quick and yet in-depth understanding of the research in uncertainty quantification for medical image analysis machine learning models.
no code implementations • 30 Mar 2023 • Yucheng Ruan, Xiang Lan, Daniel J. Tan, Hairil Rizal Abdullah, Mengling Feng
While deep learning approaches, particularly transformer-based models, have shown remarkable performance in tabular data prediction, there are still problems remained for existing work to be effectively adapted into medical domain, such as under-utilization of unstructured free-texts, limited exploration of textual information in structured data, and data corruption.
1 code implementation • 7 Feb 2023 • Xiang Lan, Hanshu Yan, Shenda Hong, Mengling Feng
In this paper, we study two types of bad positive pairs that can impair the quality of time series representation learned through contrastive learning: the noisy positive pair and the faulty positive pair.
no code implementations • 29 Jul 2022 • Young Seok Jeon, Hongfei Yang, Mengling Feng
In this work, we propose a Fourier Coefficient Segmentation Network~(FCSN) -- a novel DNN-based model that segments an object by learning the complex Fourier coefficients of the object's masks.
no code implementations • 16 Apr 2022 • Jiangeng Chang, Yucheng Ruan, Cui Shaoze, John Soong Tshon Yit, Mengling Feng
We suggested a unified system with core components of data augmentation, ImageNet-pretrained ResNet-50, cost-sensitive loss, deep ensemble learning, and uncertainty estimation to quickly and consistently detect COVID-19 using acoustic evidence.
no code implementations • 18 Sep 2021 • Xiang Lan, Dianwen Ng, Shenda Hong, Mengling Feng
In inter subject self-supervision, we design a set of data augmentations according to the clinical characteristics of cardiac signals and perform contrastive learning among subjects to learn distinctive representations for various types of patients.
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.
no code implementations • 11 Jul 2021 • Jiangeng Chang, Shaoze Cui, Mengling Feng
In this paper, we propose a deep residual network-based method, namely the DiCOVA-Net, to identify COVID-19 infected patients based on the acoustic recording of their coughs.
no code implementations • 14 May 2021 • Hao Du, Melissa Min-Szu Yao, Liangyu Chen, Wing P. Chan, Mengling Feng
In this study, we proposed a multi-task deep graph convolutional network (GCN) method for the automatic characterization of morphology and distribution of microcalcifications in mammograms.
no code implementations • 12 Dec 2020 • Zhaowei Zhu, Xiang Lan, Tingting Zhao, Yangming Guo, Pipin Kojodjojo, Zhuoyang Xu, Zhuo Liu, SiQi Liu, Han Wang, Xingzhi Sun, Mengling Feng
Cardiovascular disease is a major threat to health and one of the primary causes of death globally.
no code implementations • 16 Dec 2019 • Hao Du, Jiashi Feng, Mengling Feng
In clinical practice, human radiologists actually review medical images with high resolution monitors and zoom into region of interests (ROIs) for a close-up examination.
no code implementations • 22 Jul 2019 • Si-Qi Liu, Kee Yuan Ngiam, Mengling Feng
Owe to the recent advancements in Artificial Intelligence especially deep learning, many data-driven decision support systems have been implemented to facilitate medical doctors in delivering personalized care.
no code implementations • 14 Jan 2019 • Ziyuan Pan, Hao Du, Kee Yuan Ngiam, Fei Wang, Ping Shum, Mengling Feng
Compared with the existing models, our method has a number of distinct features: we utilized the accumulative data of patients in ICU; we developed a self-correcting mechanism that feeds errors from the previous predictions back into the network; we also proposed a regularization method that takes into account not only the model's prediction error on the label but also its estimation errors on the input data.
2 code implementations • Nature 2016 • Alistair E.W. Johnson, Tom J. Pollard, Lu Shen, Li-wei H. Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, Roger G. Mark
MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital.