452 citations · 1.4k across the 40 of their papers we have counts for
8 papers · 1 filter
3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training
Yingda Xia, Fengze Liu, Dong Yang +6
While making a tremendous impact in various fields, deep neural networks usually require large amounts of labeled data for training which are expensive to collect in many applicati…
3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation
Masahiro Oda, Natsuki Shimizu, Holger R. Roth +6
This paper presents a fully automated atlas-based pancreas segmentation method from CT volumes utilizing 3D fully convolutional network (FCN) feature-based pancreas localization. S…
A multi-scale pyramid of 3D fully convolutional networks for abdominal multi-organ segmentation
Holger R. Roth, Chen Shen, Hirohisa Oda +5
Recent advances in deep learning, like 3D fully convolutional networks (FCNs), have improved the state-of-the-art in dense semantic segmentation of medical images. However, most ne…
Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning
Takayasu Moriya, Holger R. Roth, Shota Nakamura +4
This paper presents a novel unsupervised segmentation method for 3D medical images. Convolutional neural networks (CNNs) have brought significant advances in image segmentation. Ho…
Unsupervised Pathology Image Segmentation Using Representation Learning with Spherical K-means
Takayasu Moriya, Holger R. Roth, Shota Nakamura +4
This paper presents a novel method for unsupervised segmentation of pathology images. Staging of lung cancer is a major factor of prognosis. Measuring the maximum dimensions of the…
Deep learning and its application to medical image segmentation
Holger R. Roth, Chen Shen, Hirohisa Oda +4
One of the most common tasks in medical imaging is semantic segmentation. Achieving this segmentation automatically has been an active area of research, but the task has been prove…