Chee Seng Chan

University of Malaya

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Paolo Remagnino

Durham University

Honghai Liu

Harbin Institute of Technology

Kam Woh Ng

University of Surrey

Lixin Fan

Xi'an Jiaotong University

Jian Han Lim

University of Malaya

Chun Chet Ng

University of Malaya

Qiang Yang

Hong Kong University of Science and Technology

Zhaojie Ju

University of Portsmouth

Mehdi Khoury

University of Exeter

Xiaofei Ji

Shenyang Aerospace University

Cooperative Institutions

University of Malaya

106

Genome Institute of Singapore

Agency for Science, Technology and Research

National University of Singapore

Chinese Academy of Sciences

Zhejiang University

Nanyang Technological University

University of Chinese Academy of Sciences

University of Hong Kong

Information Technology University

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Accelerated implementation of teleconsultation services for the monitoring of oral potentially malignant disorders as a result of the COVID-19 pandemic

Oral Oncology (2022)

Senthilmani Rajendran Zuraiza Mohamad Zaini Ying Zhi Lim Thomas George Kallarakkal Anand Ramanathan

Pandemic

2019-20 coronavirus outbreak

10.1016/j.oraloncology.2022.105730

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Citations (4)

Human Arm-Motion Classification Using Qualitative Normalised Templates

Lecture notes in computer science (2006)

Chee Seng Chan Honghai Liu David Brown

Template

10.1007/11892960_77

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Citations (2)

Computational Intelligence for Human Interactive Communication of Robot Partners

Lecture notes in computer science (2012)

Naoki Masuyama Chee Seng Chan Naoyuki Kuobota Jinseok Woo

Human–robot interaction

10.1007/978-3-642-32695-0_71

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Citations (2)

Recurrent Fusion Genes in Gastric Cancer: CLDN18-ARHGAP26 Induces Loss of Epithelial Integrity

Cell Reports (2015)

Fei Yao Jaya P. Kausalya Yee Yen Sia Audrey S.M. Teo Wah Heng Lee

Genome rearrangements, a hallmark of cancer, can result in gene fusions with oncogenic properties. Using DNA paired-end-tag (DNA-PET) whole-genome sequencing, we analyzed 15 gastric cancers (GCs) from Southeast Asians. Rearrangements were enriched in open chromatin and shaped by chromatin structure. We identified seven rearrangement hot spots and 136 gene fusions. In three out of 100 GC cases, we found recurrent fusions between CLDN18, a tight junction gene, and ARHGAP26, a gene encoding a RHOA inhibitor. Epithelial cell lines expressing CLDN18-ARHGAP26 displayed a dramatic loss of epithelial phenotype and long protrusions indicative of epithelial-mesenchymal transition (EMT). Fusion-positive cell lines showed impaired barrier properties, reduced cell-cell and cell-extracellular matrix adhesion, retarded wound healing, and inhibition of RHOA. Gain of invasion was seen in cancer cell lines expressing the fusion. Thus, CLDN18-ARHGAP26 mediates epithelial disintegration, possibly leading to stomach H+ leakage, and the fusion might contribute to invasiveness once a cell is transformed.

10.1016/j.celrep.2015.06.020

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Citations (131)

Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding

Wai Lam Hoo Tae‐Kyun Kim Yuru Pei Chee Seng Chan

Image understanding is an important research domain in the computer vision due to its wide real-world applications. For an image understanding framework that uses the Bag-of-Words model representation, the visual codebook is an essential part. Random forest (RF) as a tree-structure discriminative codebook has been a popular choice. However, the performance of the RF can be degraded if the local patch labels are poorly assigned. In this paper, we tackle this problem by a novel way to update the RF codebook learning for a more discriminative codebook with the introduction of the soft class labels, estimated from the pLSA model based on a feedback scheme. The feedback scheme is performed on both the image and patch levels respectively, which is in contrast to the state-of-the-art RF codebook learning that focused on either image or patch level only. Experiments on 15-Scene and C-Pascal datasets had shown the effectiveness of the proposed method in image understanding task.

Discriminative model

Tree (set theory)

Pascal (unit)

10.1109/icpr.2014.592

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Citations (4)

Digital Watermarking for Machine Learning Model

Lixin Fan Chee Seng Chan Qiang Yang

The first book to address the motivations, techniques and protocols of machine learning model ownership verification using digital watermarking.

10.1007/978-981-19-7554-7

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Citations (1)

ICDAR 2021 Competition on Integrated Circuit Text Spotting and Aesthetic Assessment

Lecture notes in computer science (2021)

Chun Chet Ng Akmalul Khairi Bin Nazaruddin Yeong Khang Lee Xinyu Wang Yuliang Liu

Spotting

Foundation (evidence)

10.1007/978-3-030-86337-1_44

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Citations (2)

Skin-based privacy filter for surveillance systems

Wai Lam Hoo Alina Miron Atta Badii Chee Seng Chan

With surveillance monitoring becoming widely available with the emergence of high resolution cameras, privacy concerns have been raised. One way to achieve privacy protection is by employing a privacy preserving device that can protect persons' identity, but still provide sufficient information to detect anomalous events if necessary. The skin region is important as a Personally Identifying Information (PII) that needs to be obscured if privacy is to be protected. Thus, this paper presents skin detection techniques to conceal privacy sensitive information. However, state-of-the-art skin detection methods suffer from various problems when deployed in a surveillance system. Accordingly, we propose a solution whereby we first find a set of candidate Regions-of-Interest (RoI) and then apply skin detection to the RoI thus found so as to locate the skin. By using this strategy, skin regions can be well delineated to allow targeted privacy filtering without covering the non-RoI parts.

Privacy Protection

Information sensitivity

10.1109/iwssip.2015.7314228

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Citations (2)

Keybook: Unbias object recognition using keywords

Expert Systems with Applications (2015)

Wai Lam Hoo Chern Hong Lim Chee Seng Chan

Intuition

Undo

10.1016/j.eswa.2015.01.019

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Citations (4)

Anisotropic Partial Differential Equation Based Video Saliency Detection

Wai Lam Hoo Chee Seng Chan

In this paper, we propose a novel video saliency detection method using the Partial Differential Equations (PDEs). We first form a static adaptive anisotropic PDE model from the unpredicted frames of the video using a detection map and a saliency seeds set of most attractive image elements. At the same time, we also extract motion features from the predicted frames of the video to generate motion saliency map. Then, we combine these two maps to obtain the final saliency map (video). Experiments on various human-action datasets show that our video saliency detection model performs favourably against the conventional solutions.

Motion Detection

Kadir–Brady saliency detector

10.1109/icip.2018.8451622

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Citations (0)