no code implementations • 3 Jul 2023 • Gabriel Tjio, Ping Liu, Yawei Luo, Chee Keong Kwoh, Joey Zhou Tianyi
Our workflow generates target-like images using the noisy predictions from the original target domain images.
no code implementations • 18 Apr 2023 • Gabriel Tjio, Ping Liu, Chee-Keong Kwoh, Joey Tianyi Zhou
To tackle this challenge, we introduce a dual-stage Feature Transform (dFT) layer within the Adversarial Semantic Hallucination+ (ASH+) framework.
1 code implementation • 8 Jun 2021 • Gabriel Tjio, Ping Liu, Joey Tianyi Zhou, Rick Siow Mong Goh
In this work, we propose an adversarial semantic hallucination approach (ASH), which combines a class-conditioned hallucination module and a semantic segmentation module.
no code implementations • 18 Apr 2020 • Gabriel Tjio, Xulei Yang, Jia Mei Hong, Sum Thai Wong, Vanessa Ding, Andre Choo, Yi Su
Our approach was approximately 100 times faster than the original DCNN approach while simultaneously preserving high accuracy and precision.
no code implementations • 4 Jul 2019 • Shaohua Li, Yong liu, Xiuchao Sui, Cheng Chen, Gabriel Tjio, Daniel Shu Wei Ting, Rick Siow Mong Goh
Deep learning for medical image classification faces three major challenges: 1) the number of annotated medical images for training are usually small; 2) regions of interest (ROIs) are relatively small with unclear boundaries in the whole medical images, and may appear in arbitrary positions across the x, y (and also z in 3D images) dimensions.