44 citations · 64 across the 5 of their papers we have counts for
5 papers
An End-to-End learnable Flow Regularized Model for Brain Tumor Segmentation
Yan Shen, Zhanghexuan Ji, Mingchen Gao
Many segmentation tasks for biomedical images can be modeled as the minimization of an energy function and solved by a class of max-flow and min-cut optimization algorithms. Howeve…
Dynamic Routing on Deep Neural Network for Thoracic Disease Classification and Sensitive Area Localization
Yan Shen, Mingchen Gao
We present and evaluate a new deep neural network architecture for automatic thoracic disease detection on chest X-rays. Deep neural networks have shown great success in a plethora…
Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling
Mingchen Gao, Ziyue Xu, Le Lu +3
Accurately predicting and detecting interstitial lung disease (ILD) patterns given any computed tomography (CT) slice without any pre-processing prerequisites, such as manually del…
Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features
Mario Buty, Ziyue Xu, Mingchen Gao +3
Computed tomography imaging is a standard modality for detecting and assessing lung cancer. In order to evaluate the malignancy of lung nodules, clinical practice often involves ex…
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Hoo-Chang Shin, Holger R. Roth, Mingchen Gao +6
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and the revival of deep CNN. CNNs enable learning data-d…