A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases

1 code implementation • 17 Jun 2018 • C. -H. Huck Yang, Jia-Hong Huang, Fangyu Liu, Fang-Yi Chiu, Mengya Gao, Weifeng Lyu, I-Hung Lin M. D., Jesper Tegner

Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists.

BIG-bench Machine Learning General Classification

Learning Functions in Large Networks requires Modularity and produces Multi-Agent Dynamics

2 code implementations • 9 Jul 2018 • C. -H. Huck Yang, Rise Ooi, Tom Hiscock, Victor Eguiluz, Jesper Tegnér

We ask whether larger dynamical network motifs exist in biological networks, thus contributing to the higher-order organization of a network.

Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model

1 code implementation • 16 Aug 2018 • C. -H. Huck Yang, Fangyu Liu, Jia-Hong Huang, Meng Tian, Hiromasa Morikawa, I-Hung Lin, Yi-Chieh Liu, Hao-Hsiang Yang, Jesper Tegner

Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists.

General Classification

Robust Unsupervised Multi-Object Tracking in Noisy Environments

1 code implementation • 20 May 2021 • C. -H. Huck Yang, Mohit Chhabra, Y. -C. Liu, Quan Kong, Tomoaki Yoshinaga, Tomokazu Murakami

We resolve this problem by introducing a robust unsupervised multi-object tracking (MOT) model: AttU-Net.

Multi-Object Tracking Object +1