1 code implementation • 17 Jun 2021 • Haiping Lu, Xianyuan Liu, Robert Turner, Peizhen Bai, Raivo E Koot, Shuo Zhou, Mustafa Chasmai, Lawrence Schobs
Machine learning is a general-purpose technology holding promises for many interdisciplinary research problems.
2 code implementations • 4 Mar 2022 • Lawrence Schobs, Andrew J. Swift, Haiping Lu
We propose Quantile Binning, a data-driven method to categorize predictions by uncertainty with estimated error bounds.
2 code implementations • 6 Apr 2024 • Prasun C Tripathi, Sina Tabakhi, Mohammod N I Suvon, Lawrence Schöb, Samer Alabed, Andrew J Swift, Shuo Zhou, Haiping Lu
We extract spatio-temporal features from CMR scans using tensor-based learning.
2 code implementations • 3 Aug 2022 • Peizhen Bai, Filip Miljković, Bino John, Haiping Lu
Recent deep learning-based methods show promising performance but two challenges remain: (i) how to explicitly model and learn local interactions between drugs and targets for better prediction and interpretation; (ii) how to generalize prediction performance on novel drug-target pairs from different distribution.
1 code implementation • 28 Jan 2020 • Hao Xu, Shengqi Sang, Haiping Lu
The use of drug combinations often leads to polypharmacy side effects (POSE).
Ranked #1 on Pose Prediction on SUN-Mem
1 code implementation • 29 Oct 2020 • Hao Xu, Shengqi Sang, Peizhen Bai, Laurence Yang, Haiping Lu
Heterogeneous graph representation learning aims to learn low-dimensional vector representations of different types of entities and relations to empower downstream tasks.
2 code implementations • 30 Nov 2018 • Li Zhang, Heda Song, Nikolaos Aletras, Haiping Lu
Graph convolutional network (GCN) is an emerging neural network approach.
1 code implementation • 14 Mar 2023 • Prasun C. Tripathi, Mohammod N. I. Suvon, Lawrence Schobs, Shuo Zhou, Samer Alabed, Andrew J. Swift, Haiping Lu
In this work, we develop a tensor learning-based pipeline for identifying PAWP from multimodal cardiac Magnetic Resonance Imaging (MRI).
1 code implementation • 8 Apr 2024 • Shuo Zhou, Junhao Luo, Yaya Jiang, Haolin Wang, Haiping Lu, Gaolang Gong
Here, we formulate modeling sex differences in lateralization of functional networks as a dual-classification problem, consisting of first-order classification for left vs. right functional networks and second-order classification for male vs. female models.
no code implementations • 30 Apr 2015 • Qiquan Shi, Haiping Lu
However, under the TVP setting, it is difficult to develop an effective multilinear PCA method with the orthogonality constraint.
no code implementations • 4 Dec 2018 • Wenwen Li, Jian Lou, Shuo Zhou, Haiping Lu
While functional magnetic resonance imaging (fMRI) is important for healthcare/neuroscience applications, it is challenging to classify or interpret due to its multi-dimensional structure, high dimensionality, and small number of samples available.
1 code implementation • 25 Dec 2018 • Yan Ge, Haiping Lu, Pan Peng
This paper proposes a new Mixed-Order Spectral Clustering (MOSC) approach to model both second-order and third-order structures simultaneously, with two MOSC methods developed based on Graph Laplacian (GL) and Random Walks (RW).
no code implementations • 26 Mar 2019 • Shuo Zhou, Wenwen Li, Christopher R. Cox, Haiping Lu
We use public data to construct 13 transfer learning tasks in brain decoding, including three interesting multi-source transfer tasks.
no code implementations • 5 Jun 2020 • Markus D. Schirmer, Archana Venkataraman, Islem Rekik, Minjeong Kim, Stewart H. Mostofsky, Mary Beth Nebel, Keri Rosch, Karen Seymour, Deana Crocetti, Hassna Irzan, Michael Hütel, Sebastien Ourselin, Neil Marlow, Andrew Melbourne, Egor Levchenko, Shuo Zhou, Mwiza Kunda, Haiping Lu, Nicha C. Dvornek, Juntang Zhuang, Gideon Pinto, Sandip Samal, Jennings Zhang, Jorge L. Bernal-Rusiel, Rudolph Pienaar, Ai Wern Chung
A second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing.
no code implementations • 21 Dec 2020 • Li Zhang, Yan Ge, Haiping Lu
Graph Neural Networks (GNNs) are widely used in graph representation learning.
no code implementations • 21 Dec 2020 • Yan Ge, Jun Ma, Li Zhang, Haiping Lu
Because H2NT can sparsify networks with motif structures, it can also improve the computational efficiency of existing network embedding methods when integrated.
no code implementations • 22 Jun 2021 • Xianyuan Liu, Raivo Koot, Shuo Zhou, Tao Lei, Haiping Lu
Under the team name xy9, our submission achieved 5th place in terms of top-1 accuracy for verb class and all top-5 accuracies.
no code implementations • 1 Jul 2021 • Raivo Koot, Haiping Lu
Efficient video action recognition remains a challenging problem.
no code implementations • 17 Aug 2021 • Xianyuan Liu, Shuo Zhou, Tao Lei, Haiping Lu
Finally, we propose a Channel-Temporal Attention Network (CTAN) to integrate these blocks into existing architectures.
no code implementations • 18 Nov 2021 • Raivo Koot, Markus Hennerbichler, Haiping Lu
Our experiments conclude that current video transformers are not yet capable of lightweight action recognition on par with traditional convolutional baselines, and that the previously mentioned shortcomings need to be addressed to bridge this gap.
no code implementations • 27 Jun 2022 • Li Zhang, Yan Ge, Jun Ma, Jianmo Ni, Haiping Lu
In this paper, we propose to incorporate the knowledge graph (KG) for CDR, which enables items in different domains to share knowledge.
no code implementations • 29 Nov 2022 • Sina Tabakhi, Mohammod Naimul Islam Suvon, Pegah Ahadian, Haiping Lu
However, several challenges are hindering the development in this area, including the availability of easily accessible open-source tools.
no code implementations • 9 May 2023 • Pawel Pukowski, Haiping Lu
That is why we introduce SkelEx, an algorithm to extract a skeleton of the membership functions learned by ReLU NNs, making those functions easier to interpret and analyze.
no code implementations • 1 Sep 2023 • Peizhen Bai, Xianyuan Liu, Haiping Lu
Owing to the scarcity of labeled molecules, there has been growing interest in self-supervised learning methods that learn generalizable molecular representations from unlabeled data.
no code implementations • 15 Mar 2024 • Wenrui Fan, Mohammod Naimul Islam Suvon, Shuo Zhou, Xianyuan Liu, Samer Alabed, Venet Osmani, Andrew Swift, Chen Chen, Haiping Lu
Moreover, a novel vision-language Prototypical Contr-astive Learning (ProtoCL) method is adopted in MeDSLIP to enhance the alignment within the anatomical and pathological streams.
no code implementations • 20 Mar 2024 • Mohammod N. I. Suvon, Prasun C. Tripathi, Wenrui Fan, Shuo Zhou, Xianyuan Liu, Samer Alabed, Venet Osmani, Andrew J. Swift, Chen Chen, Haiping Lu
In response to these limitations, we propose a novel multimodal variational autoencoder ($\text{CardioVAE}_\text{X, G}$) to integrate low-cost chest X-ray (CXR) and electrocardiogram (ECG) modalities with pre-training on a large unlabeled dataset.