76 citations · 335 across the 47 of their papers we have counts for
10 papers · 1 filter
Attention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs
Yuxing Tang, Xiaosong Wang, Adam P. Harrison +3
In this work, we exploit the task of joint classification and weakly supervised localization of thoracic diseases from chest radiographs, with only image-level disease labels coupl…
CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement
Youbao Tang, Jinzheng Cai, Le Lu +5
Automated lesion segmentation from computed tomography (CT) is an important and challenging task in medical image analysis. While many advancements have been made, there is room fo…
Iterative Attention Mining for Weakly Supervised Thoracic Disease Pattern Localization in Chest X-Rays
Jinzheng Cai, Le Lu, Adam P. Harrison +3
Given image labels as the only supervisory signal, we focus on harvesting, or mining, thoracic disease localizations from chest X-ray images. Harvesting such localizations from exi…
Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST
Jinzheng Cai, Youbao Tang, Le Lu +5
Volumetric lesion segmentation from computed tomography (CT) images is a powerful means to precisely assess multiple time-point lesion/tumor changes. However, because manual 3D seg…
Semi-Automatic RECIST Labeling on CT Scans with Cascaded Convolutional Neural Networks
Youbao Tang, Adam P. Harrison, Mohammadhadi Bagheri +2
Response evaluation criteria in solid tumors (RECIST) is the standard measurement for tumor extent to evaluate treatment responses in cancer patients. As such, RECIST annotations m…
CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation
Dakai Jin, Ziyue Xu, Youbao Tang +2
Data availability plays a critical role for the performance of deep learning systems. This challenge is especially acute within the medical image domain, particularly when patholog…
White matter hyperintensity segmentation from T1 and FLAIR images using fully convolutional neural networks enhanced with residual connections
Dakai Jin, Ziyue Xu, Adam P. Harrison +1
Segmentation and quantification of white matter hyperintensities (WMHs) are of great importance in studying and understanding various neurological and geriatric disorders. Although…
Random Hinge Forest for Differentiable Learning
Nathan Lay, Adam P. Harrison, Sharon Schreiber +2
We propose random hinge forests, a simple, efficient, and novel variant of decision forests. Importantly, random hinge forests can be readily incorporated as a general component wi…
High Performance Rearrangement and Multiplication Routines for Sparse Tensor Arithmetic
Adam P. Harrison, Dileepan Joseph
Researchers are increasingly incorporating numeric high-order data, i.e., numeric tensors, within their practice. Just like the matrix/vector (MV) paradigm, the development of mult…
Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST
Jinzheng Cai, Youbao Tang, Le Lu +5
Volumetric lesion segmentation via medical imaging is a powerful means to precisely assess multiple time-point lesion/tumor changes. Because manual 3D segmentation is prohibitively…