24 citations · 50 across the 8 of their papers we have counts for
10 papers · 1 filter
Multi-Modality Information Fusion for Radiomics-based Neural Architecture Search
Yige Peng, Lei Bi, Michael Fulham +2
'Radiomics' is a method that extracts mineable quantitative features from radiographic images. These features can then be used to determine prognosis, for example, predicting the d…
Semi-supervised estimation of event temporal length for cell event detection
Ha Tran Hong Phan, Ashnil Kumar, David Feng +2
Cell event detection in cell videos is essential for monitoring of cellular behavior over extended time periods. Deep learning methods have shown great success in the detection of…
Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification
Euijoon Ahn, Ashnil Kumar, Dagan Feng +2
Medical image analysis using supervised deep learning methods remains problematic because of the reliance of deep learning methods on large amounts of labelled training data. Altho…
Unsupervised Deep Transfer Feature Learning for Medical Image Classification
Euijoon Ahn, Ashnil Kumar, Dagan Feng +2
The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale, annotated training data. However, there is a pau…
Automated Segmentation of the Optic Disk and Cup using Dual-Stage Fully Convolutional Networks
Lei Bi, Yuyu Guo, Qian Wang +3
Automated segmentation of the optic cup and disk on retinal fundus images is fundamental for the automated detection / analysis of glaucoma. Traditional segmentation approaches dep…
Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer
Ashnil Kumar, Michael Fulham, Dagan Feng +1
The analysis of multi-modality positron emission tomography and computed tomography (PET-CT) images for computer aided diagnosis applications requires combining the sensitivity of…