activity
20192021
most citedEnhancing the Extraction of Interpretable Information for Ischemic Stroke Imaging from Deep Neural Networks

9 citations · 13 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2021

A Modified Convolutional Network for Auto-encoding based on Pattern Theory Growth Function

Erico Tjoa

This brief paper reports the shortcoming of a variant of convolutional neural network whose components are developed based on the pattern theory framework.

cs.LG20214 cited

Convolutional Neural Network Interpretability with General Pattern Theory

Erico Tjoa, Guan Cuntai

Ongoing efforts to understand deep neural networks (DNN) have provided many insights, but DNNs remain incompletely understood. Improving DNN's interpretability has practical benefi…

cs.CV2020

Generalization on the Enhancement of Layerwise Relevance Interpretability of Deep Neural Network

Erico Tjoa, Guan Cuntai

The practical application of deep neural networks are still limited by their lack of transparency. One of the efforts to provide explanation for decisions made by artificial intell…

eess.IV20199 cited

Enhancing the Extraction of Interpretable Information for Ischemic Stroke Imaging from Deep Neural Networks

Erico Tjoa, Guo Heng, Lu Yuhao +1

We implement a visual interpretability method Layer-wise Relevance Propagation (LRP) on top of 3D U-Net trained to perform lesion segmentation on the small dataset of multi-modal i…

cs.LG2019

A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI

Erico Tjoa, Cuntai Guan

Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, esp…