82 citations · 192 across the 9 of their papers we have counts for
9 papers
A new Time-decay Radiomics Integrated Network (TRINet) for short-term breast cancer risk prediction
Hong Hui Yeoh, Fredrik Strand, Raphaël Phan +2
To facilitate early detection of breast cancer, there is a need to develop short-term risk prediction schemes that can prescribe personalized/individualized screening mammography r…
RADIFUSION: A multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement
Hong Hui Yeoh, Andrea Liew, Raphaël Phan +5
Breast cancer is a significant public health concern and early detection is critical for triaging high risk patients. Sequential screening mammograms can provide important spatiote…
CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation
Andrea Liew, Chun Cheng Lee, Boon Leong Lan +1
Convolutional neural networks (CNNs) have been used quite successfully for semantic segmentation of brain tumors. However, current CNNs and attention mechanisms are stochastic in n…
A new semi-supervised self-training method for lung cancer prediction
Kelvin Shak, Mundher Al-Shabi, Andrea Liew +4
Background and Objective: Early detection of lung cancer is crucial as it has high mortality rate with patients commonly present with the disease at stage 3 and above. There are on…
3D Axial-Attention for Lung Nodule Classification
Mundher Al-Shabi, Kelvin Shak, Maxine Tan
Purpose: In recent years, Non-Local based methods have been successfully applied to lung nodule classification. However, these methods offer 2D attention or limited 3D attention to…
ProCAN: Progressive Growing Channel Attentive Non-Local Network for Lung Nodule Classification
Mundher Al-Shabi, Kelvin Shak, Maxine Tan
Lung cancer classification in screening computed tomography (CT) scans is one of the most crucial tasks for early detection of this disease. Many lives can be saved if we are able…
Cribriform pattern detection in prostate histopathological images using deep learning models
Malay Singh, Emarene Mationg Kalaw, Wang Jie +7
Architecture, size, and shape of glands are most important patterns used by pathologists for assessment of cancer malignancy in prostate histopathological tissue slides. Varying st…
Lung Nodule Classification using Deep Local-Global Networks
Mundher Al-Shabi, Boon Leong Lan, Wai Yee Chan +2
Purpose: Lung nodules have very diverse shapes and sizes, which makes classifying them as benign/malignant a challenging problem. In this paper, we propose a novel method to predic…
Gated-Dilated Networks for Lung Nodule Classification in CT scans
Mundher Al-Shabi, Hwee Kuan Lee, Maxine Tan
Different types of Convolutional Neural Networks (CNNs) have been applied to detect cancerous lung nodules from computed tomography (CT) scans. However, the size of a nodule is ver…