no code implementations • 1 Apr 2023 • Hong Hui Yeoh, Andrea Liew, Raphaël Phan, Fredrik Strand, Kartini Rahmat, Tuong Linh Nguyen, John L. Hopper, Maxine Tan
Sequential screening mammograms can provide important spatiotemporal information about changes in breast tissue over time.
no code implementations • 8 Jul 2021 • Andrea Liew, Chun Cheng Lee, Boon Leong Lan, Maxine Tan
Furthermore, driven by the scarcity of brain tumor data, we investigate the Noisy Student method for segmentation tasks.
no code implementations • 28 Dec 2020 • Mundher Al-Shabi, Kelvin Shak, Maxine Tan
Methods: We propose to use 3D Axial-Attention, which requires a fraction of the computing power of a regular Non-Local network (i. e., self-attention).
no code implementations • 17 Dec 2020 • Kelvin Shak, Mundher Al-Shabi, Andrea Liew, Boon Leong Lan, Wai Yee Chan, Kwan Hoong Ng, Maxine Tan
This study presents a complete end-to-end scheme to detect and classify lung nodules using the state-of-the-art Self-training with Noisy Student method on a comprehensive CT lung screening dataset of around 4, 000 CT scans.
no code implementations • 29 Oct 2020 • Mundher Al-Shabi, Kelvin Shak, Maxine Tan
Nevertheless, the large variation in the size and heterogeneous appearance of the nodules makes this task an extremely challenging one.
Ranked #2 on Lung Nodule Classification on LIDC-IDRI
no code implementations • 9 Oct 2019 • Malay Singh, Emarene Mationg Kalaw, Wang Jie, Mundher Al-Shabi, Chin Fong Wong, Danilo Medina Giron, Kian-Tai Chong, Maxine Tan, Zeng Zeng, Hwee Kuan Lee
In this paper, we present an annotated cribriform dataset along with analysis of deep learning models and hand-crafted features for cribriform pattern detection in prostate histopathological images.
1 code implementation • 23 Apr 2019 • Mundher Al-Shabi, Boon Leong Lan, Wai Yee Chan, Kwan-Hoong Ng, Maxine Tan
In this paper, we propose a novel method to predict the malignancy of nodules that have the capability to analyze the shape and size of a nodule using a global feature extractor, as well as the density and structure of the nodule using a local feature extractor.
Ranked #6 on Lung Nodule Classification on LIDC-IDRI
no code implementations • 1 Jan 2019 • 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.
Ranked #3 on Lung Nodule Classification on LIDC-IDRI