141 citations · 226 across the 15 of their papers we have counts for
15 papers
CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects
Ange Lou, Shuyue Guan, Murray Loew
Segmenting medical images accurately and reliably is important for disease diagnosis and treatment. It is a challenging task because of the wide variety of objects' sizes, shapes,…
Informing selection of performance metrics for medical image segmentation evaluation using configurable synthetic errors
Shuyue Guan, Ravi K. Samala, Weijie Chen
Machine learning-based segmentation in medical imaging is widely used in clinical applications from diagnostics to radiotherapy treatment planning. Segmented medical images with gr…
A Sneak Attack on Segmentation of Medical Images Using Deep Neural Network Classifiers
Shuyue Guan, Murray Loew
Instead of using current deep-learning segmentation models (like the UNet and variants), we approach the segmentation problem using trained Convolutional Neural Network (CNN) class…
A Novel Intrinsic Measure of Data Separability
Shuyue Guan, Murray Loew
In machine learning, the performance of a classifier depends on both the classifier model and the separability/complexity of datasets. To quantitatively measure the separability of…
CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects
Ange Lou, Shuyue Guan, Hanseok Ko +1
Segmenting medical images accurately and reliably is important for disease diagnosis and treatment. It is a challenging task because of the wide variety of objects' sizes, shapes,…
A Distance-based Separability Measure for Internal Cluster Validation
Shuyue Guan, Murray Loew
To evaluate clustering results is a significant part of cluster analysis. Since there are no true class labels for clustering in typical unsupervised learning, many internal cluste…
CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation
Ange Lou, Shuyue Guan, Murray Loew
Currently, developments of deep learning techniques are providing instrumental to identify, classify, and quantify patterns in medical images. Segmentation is one of the important…
Understanding the Ability of Deep Neural Networks to Count Connected Components in Images
Shuyue Guan, Murray Loew
Humans can count very fast by subitizing, but slow substantially as the number of objects increases. Previous studies have shown a trained deep neural network (DNN) detector can co…
Segmentation of Infrared Breast Images Using MultiResUnet Neural Network
Ange Lou, Shuyue Guan, Nada Kamona +1
Breast cancer is the second leading cause of death for women in the U.S. Early detection of breast cancer is key to higher survival rates of breast cancer patients. We are investig…
The training accuracy of two-layer neural networks: its estimation and understanding using random datasets
Shuyue Guan, Murray Loew
Although the neural network (NN) technique plays an important role in machine learning, understanding the mechanism of NN models and the transparency of deep learning still require…
Analysis of Generalizability of Deep Neural Networks Based on the Complexity of Decision Boundary
Shuyue Guan, Murray Loew
For supervised learning models, the analysis of generalization ability (generalizability) is vital because the generalizability expresses how well a model will perform on unseen da…
An Internal Cluster Validity Index Using a Distance-based Separability Measure
Shuyue Guan, Murray Loew
To evaluate clustering results is a significant part of cluster analysis. There are no true class labels for clustering in typical unsupervised learning. Thus, a number of internal…
DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation
Ange Lou, Shuyue Guan, Murray Loew
Recently, deep learning has become much more popular in computer vision area. The Convolution Neural Network (CNN) has brought a breakthrough in images segmentation areas, especial…
Data Separability for Neural Network Classifiers and the Development of a Separability Index
Shuyue Guan, Murray Loew, Hanseok Ko
In machine learning, the performance of a classifier depends on both the classifier model and the dataset. For a specific neural network classifier, the training process varies wit…
A Novel Measure to Evaluate Generative Adversarial Networks Based on Direct Analysis of Generated Images
Shuyue Guan, Murray Loew
The Generative Adversarial Network (GAN) is a state-of-the-art technique in the field of deep learning. A number of recent papers address the theory and applications of GANs in var…