3D Axial-Attention for Lung Nodule Classification

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).

Classification General Classification +1

A new semi-supervised self-training method for lung cancer prediction

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.

Computed Tomography (CT)

ProCAN: Progressive Growing Channel Attentive Non-Local Network for Lung Nodule Classification

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.

Computed Tomography (CT) General Classification +1