7 Result(s)
-
Chapter and Conference Paper
CASIA-onDo: A New Database for Online Handwritten Document Analysis
In this paper we introduce an online handwritten document database (CASIA-onDo), serving as a standard database for the development and evaluation of methods in the field of online handwritten document layout ...
-
Chapter and Conference Paper
Region Ensemble Network for MCI Conversion Prediction with a Relation Regularized Loss
Despite many recent advances, computer-aided mild cognitive impairment (MCI) conversion prediction is still a very challenging task due to: 1) the abnormal areas are subtle compared to the size of the whole br...
-
Article
Handwritten Mathematical Expression Recognition via Paired Adversarial Learning
Recognition of handwritten mathematical expressions (MEs) is an important problem that has wide applications in practice. Handwritten ME recognition is challenging due to the variety of writing styles and ME f...
-
Chapter and Conference Paper
Bag of Tricks for 3D MRI Brain Tumor Segmentation
3D brain tumor segmentation is essential for the diagnosis, monitoring, and treatment planning of brain diseases. In recent studies, the Deep Convolution Neural Network (DCNN) is one of the most potent methods...
-
Chapter and Conference Paper
Online Handwritten Diagram Recognition with Graph Attention Networks
Handwritten text recognition has been extensively researched over decades and achieved extraordinary success in recent years. However, handwritten diagram recognition is still a challenging task because of the...
-
Chapter and Conference Paper
Multi-view Semi-supervised 3D Whole Brain Segmentation with a Self-ensemble Network
Despite remarkable progress, 3D whole brain segmentation of structural magnetic resonance imaging (MRI) into a large number of regions (>100) is still difficult due to the lack of annotated data and the limit...
-
Chapter and Conference Paper
Image-to-Markup Generation via Paired Adversarial Learning
Motivated by the fact that humans can grasp semantic-invariant features shared by the same category while attention-based models focus mainly on discriminative features of each object, we propose a scalable pa...
