1 code implementation • 18 Dec 2023 • Shreeram Athreya, Ashwath Radhachandran, Vedrana Ivezić, Vivek Sant, Corey W. Arnold, William Speier
These images are compared with paired images acquired from high resolution devices to demonstrate the model's ability to generate realistic high-quality images across organ systems.
no code implementations • 8 Feb 2023 • Haoyue Zhang, Jennifer S. Polson, Eric J. Yang, Kambiz Nael, William Speier, Corey W. Arnold
This is a promising result that supports future applications of deep learning on CT and CTA for the identification of eligible AIS patients for MTB.
1 code implementation • 13 Jun 2022 • Pengxin Yu, Haoyue Zhang, Han Kang, Wen Tang, Corey W. Arnold, Rongguo Zhang
In clinical practice, anisotropic volumetric medical images with low through-plane resolution are commonly used due to short acquisition time and lower storage cost.
1 code implementation • 13 Jun 2022 • Wen Tang, Han Kang, Haoyue Zhang, Pengxin Yu, Corey W. Arnold, Rongguo Zhang
Previous methods typically lack the integration of local and global information.
no code implementations • 11 Jun 2022 • Abhejit Rajagopal, Ekaterina Redekop, Anil Kemisetti, Rushi Kulkarni, Steven Raman, Kirti Magudia, Corey W. Arnold, Peder E. Z. Larson
Early prostate cancer detection and staging from MRI are extremely challenging tasks for both radiologists and deep learning algorithms, but the potential to learn from large and diverse datasets remains a promising avenue to increase their generalization capability both within- and across clinics.
no code implementations • 17 Feb 2022 • Kexin Ding, Mu Zhou, Zichen Wang, Qiao Liu, Corey W. Arnold, Shaoting Zhang, Dimitri N. Metaxas
Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales.
1 code implementation • 4 Apr 2021 • Chao Chen, Catalina Raymond, Bill Speier, Xinyu Jin, Timothy F. Cloughesy, Dieter Enzmann, Benjamin M. Ellingson, Corey W. Arnold
To alleviate the data imbalance problem between normal tissues and the tumor regions, we introduce a local loss to improve the contribution of the tumor regions, which leads to better enhancement results on tumors.
1 code implementation • 11 Dec 2020 • Beibin Li, Ezgi Mercan, Sachin Mehta, Stevan Knezevich, Corey W. Arnold, Donald L. Weaver, Joann G. Elmore, Linda G. Shapiro
In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three-levels of features for diagnostic classification.
no code implementations • 5 Nov 2020 • Jiayun Li, Wenyuan Li, Anthony Sisk, Huihui Ye, W. Dean Wallace, William Speier, Corey W. Arnold
Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload and potentially improve inter- and intra- observer agreement.
1 code implementation • 26 Sep 2020 • Yiwen Meng, William Speier, Michael K. Ong, Corey W. Arnold
We applied the current trend of pretraining and fine-tuning on EHR data to outperform the current state-of-the-art in chronic disease prediction, and to demonstrate the underlying relation between EHR codes in the sequence.
1 code implementation • 30 May 2020 • Chao Chen, Zhihong Chen, Xinyu Jin, Lanjuan Li, William Speier, Corey W. Arnold
However, training with the global image underutilizes discriminative local information, while providing extra annotations is expensive and subjective.
no code implementations • 18 Oct 2019 • Wenyuan Li, Zichen Wang, Yuguang Yue, Jiayun Li, William Speier, Mingyuan Zhou, Corey W. Arnold
In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training.
no code implementations • 30 May 2019 • Jiayun Li, Wenyuan Li, Arkadiusz Gertych, Beatrice S. Knudsen, William Speier, Corey W. Arnold
The model achieved state-of-the-art performance for prostate cancer grading with an accuracy of 85. 11\% for classifying benign, low-grade (Gleason grade 3+3 or 3+4), and high-grade (Gleason grade 4+3 or higher) slides on an independent test set.