no code implementations • 8 Feb 2024 • Pouria Yazdian Anari, Fiona Obiezu, Nathan Lay, Fatemeh Dehghani Firouzabadi, Aditi Chaurasia, Mahshid Golagha, Shiva Singh, Fatemeh Homayounieh, Aryan Zahergivar, Stephanie Harmon, Evrim Turkbey, Rabindra Gautam, Kevin Ma, Maria Merino, Elizabeth C. Jones, Mark W. Ball, W. Marston Linehan, Baris Turkbey, Ashkan A. Malayeri
The best primary model was then used to identify tumors in the remaining 861 patients and bounding box coordinates were generated on their scans using the model.
no code implementations • 6 Jan 2023 • Pouria Yazdian Anari, Nathan Lay, Aditi Chaurasia, Nikhil Gopal, Safa Samimi, Stephanie Harmon, Rabindra Gautam, Kevin Ma, Fatemeh Dehghani Firouzabadi, Evrim Turkbey, Maria Merino, Elizabeth C. Jones, Mark W. Ball, W. Marston Linehan, Baris Turkbey, Ashkan A. Malayeri
Our 2D U-Net achieved an average ccRCC lesion detection Area under the curve (AUC) of 0. 88 and DSC scores of 0. 78, 0. 40, and 0. 46 for segmentation of the kidney, cysts, and tumors, respectively.
no code implementations • 12 Mar 2022 • Pengfei Guo, Dong Yang, Ali Hatamizadeh, An Xu, Ziyue Xu, Wenqi Li, Can Zhao, Daguang Xu, Stephanie Harmon, Evrim Turkbey, Baris Turkbey, Bradford Wood, Francesca Patella, Elvira Stellato, Gianpaolo Carrafiello, Vishal M. Patel, Holger R. Roth
Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing.
no code implementations • 6 Oct 2021 • Riddhish Bhalodia, Ali Hatamizadeh, Leo Tam, Ziyue Xu, Xiaosong Wang, Evrim Turkbey, Daguang Xu
Both the classification and localization are trained in conjunction and once trained, the model can be utilized for both the localization and characterization of pneumonia using only the input image.
no code implementations • 20 Apr 2021 • Yingda Xia, Dong Yang, Wenqi Li, Andriy Myronenko, Daguang Xu, Hirofumi Obinata, Hitoshi Mori, Peng An, Stephanie Harmon, Evrim Turkbey, Baris Turkbey, Bradford Wood, Francesca Patella, Elvira Stellato, Gianpaolo Carrafiello, Anna Ierardi, Alan Yuille, Holger Roth
In this work, we design a new data-driven approach, namely Auto-FedAvg, where aggregation weights are dynamically adjusted, depending on data distributions across data silos and the current training progress of the models.
no code implementations • 7 Apr 2021 • Ugur Demir, Ismail Irmakci, Elif Keles, Ahmet Topcu, Ziyue Xu, Concetto Spampinato, Sachin Jambawalikar, Evrim Turkbey, Baris Turkbey, Ulas Bagci
We provide an innovative visual explanation algorithm for general purpose and as an example application, we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels.
no code implementations • 23 Nov 2020 • Dong Yang, Ziyue Xu, Wenqi Li, Andriy Myronenko, Holger R. Roth, Stephanie Harmon, Sheng Xu, Baris Turkbey, Evrim Turkbey, Xiaosong Wang, Wentao Zhu, Gianpaolo Carrafiello, Francesca Patella, Maurizio Cariati, Hirofumi Obinata, Hitoshi Mori, Kaku Tamura, Peng An, Bradford J. Wood, Daguang Xu
To facilitate CT analysis, recent efforts have focused on computer-aided characterization and diagnosis, which has shown promising results.
1 code implementation • 31 Jul 2020 • Leo K. Tam, Xiaosong Wang, Evrim Turkbey, Kevin Lu, Yuhong Wen, Daguang Xu
The architectural modifications address three obstacles -- implementing a supervised vision and language detection method in a weakly supervised fashion, incorporating clinical referring expression natural language information, and generating high fidelity detections with map probabilities.
no code implementations • 22 Jun 2015 • Holger R. Roth, Le Lu, Amal Farag, Hoo-chang Shin, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i. e. superpixels.
no code implementations • 22 May 2015 • Amal Farag, Le Lu, Holger R. Roth, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
We present a bottom-up approach for pancreas segmentation in abdominal CT scans that is based on a hierarchy of information propagation by classifying image patches at different resolutions; and cascading superpixels.
no code implementations • 31 Jul 2014 • Amal Farag, Le Lu, Evrim Turkbey, Jiamin Liu, Ronald M. Summers
Organ segmentation is a prerequisite for a computer-aided diagnosis (CAD) system to detect pathologies and perform quantitative analysis.
no code implementations • 6 Jun 2014 • Holger R. Roth, Le Lu, Ari Seff, Kevin M. Cherry, Joanne Hoffman, Shijun Wang, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
and 84%/90% at 6 FP/vol.