Timothy Kline

According to our database, Timothy Kline authored at least 21 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models.

Farhad Maleki, Linda Moy, Reza Forghani, Tapotosh Ghosh, Katie L. Ovens, Steve Langer, Pouria Rouzrokh, Bardia Khosravi, Ali Ganjizadeh, Daniel Warren, Roxana Daneshjou, Mana Moassefi, Atlas Haddadi Avval, Susan Sotardi, Neil A. Tenenholtz, Felipe Kitamura, Timothy Kline

CoRR, 2024

2023

Effect of Dataset Size and Medical Image Modality on Convolutional Neural Network Model Performance for Automated Segmentation: A CT and MR Renal Tumor Imaging Study.

Harrison C. Gottlich, Adriana V. Gregory, Vidit Sharma, Abhinav Khanna, Amr U. Moustafa, Christine M. Lohse, Theodora A. Potretzke, Panagiotis Korfiatis, Aaron M. Potretzke, Aleksandar Denic, Andrew D. Rule, Naoki Takahashi, Bradley J. Erickson, Bradley C. Leibovich, Timothy L. Kline

J. Digit. Imaging, August, 2023

Developing a Machine Learning-Based Clinical Decision Support Tool for Uterine Tumor Imaging.

Darryl E. Wright, Adriana V. Gregory, Deema A. Anaam, Sepideh Yadollahi, Sumana Ramanathan, Kafayat A. Oyemade, Reem Alsibai, Heather Holmes, Harrison C. Gottlich, Cherie-Akilah G. Browne, Sarah L. Cohen Rassier, Isabel Green, Elizabeth A. Stewart, Hiroaki Takahashi, Bohyun Kim, Shannon Laughlin-Tommaso, Timothy L. Kline

CoRR, 2023

Role of Image Acquisition and Patient Phenotype Variations in Automatic Segmentation Model Generalization.

Timothy L. Kline, Sumana Ramanathan, Harrison C. Gottlich, Panagiotis Korfiatis, Adriana V. Gregory

CoRR, 2023

AI in the Loop - Functionalizing Fold Performance Disagreement to Monitor Automated Medical Image Segmentation Pipelines.

Harrison C. Gottlich, Panagiotis Korfiatis, Adriana V. Gregory, Timothy L. Kline

CoRR, 2023

A Texture Neural Network to Predict the Abnormal Brachial Plexus from Routine Magnetic Resonance Imaging.

Weiguo Cao, Benjamin Howe, Nicholas Rhodes, Sumana Ramanathan, Panagiotis Korfiatis, Kimberly Amrami, Robert Spinner, Timothy Kline

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

2022

Quantifying and Visualizing Vascular Branching Geometry with Micro-CT: Normalization of Intra- and Inter-Specimen Variations.

Timothy L. Kline

CoRR, 2022

Shape Aware Automatic Region-of-Interest Subdivisions.

Timothy L. Kline

CoRR, 2022

Reproducibility in medical image radiomic studies: contribution of dynamic histogram binning.

Darryl E. Wright, Cole Cook, Jason Klug, Panagiotis Korfiatis, Timothy L. Kline

CoRR, 2022

Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification.

Timothy L. Kline, Felipe Kitamura, Ian Pan, Amine M. Korchi, Neil A. Tenenholtz, Linda Moy, Judy Wawira Gichoya, Igor Santos, Steven Blumer, Misha Ysabel Hwang, Kim-Ann Git, Abishek Shroff, Elad Walach, George Shih, Steve Langer

CoRR, 2022

2021

Semantic Instance Segmentation of Kidney Cysts in MR Images: A Fully Automated 3D Approach Developed Through Active Learning.

Adriana V. Gregory, Deema A. Anaam, Andrew J. Vercnocke, Marie E. Edwards, Vicente E. Torres, Peter C. Harris, Bradley J. Erickson, Timothy L. Kline

J. Digit. Imaging, 2021

Improving Domain Generalization in Segmentation Models with Neural Style Transfer.

Timothy L. Kline

Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021

2019

RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning.

Kenneth A. Philbrick, Alexander D. Weston, Zeynettin Akkus, Timothy L. Kline, Panagiotis Korfiatis, Tomas Sakinis, Petro M. Kostandy, Arunnit Boonrod, Atefeh Zeinoddini, Naoki Takahashi, Bradley J. Erickson

J. Digit. Imaging, 2019

Segmenting New Image Acquisitions Without Labels.

Timothy L. Kline

Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, 2019

2018

Evaluation of a deep learning architecture for MR imaging prediction of ATRX in glioma patients.

Panagiotis Korfiatis, Timothy L. Kline, Bradley J. Erickson

Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018

2017

Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status.

Panagiotis Korfiatis, Timothy L. Kline, Daniel H. Lachance, Ian F. Parney, Jan C. Buckner, Bradley J. Erickson

J. Digit. Imaging, 2017

Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys.

Timothy L. Kline, Panagiotis Korfiatis, Marie E. Edwards, Jaime D. Blais, Frank S. Czerwiec, Peter C. Harris, Bernard F. King, Vicente E. Torres, Bradley J. Erickson

J. Digit. Imaging, 2017

Toolkits and Libraries for Deep Learning.

Bradley J. Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy Kline, Kenneth Philbrick

J. Digit. Imaging, 2017

2016

Predicting 1p19q Chromosomal Deletion of Low-Grade Gliomas from MR Images using Deep Learning.

Zeynettin Akkus, Issa Ali, Jirí Sedlár, Timothy L. Kline, Jay P. Agrawal, Ian F. Parney, Caterina Giannini, Bradley J. Erickson

CoRR, 2016

2011

Algorithm-Enabled Low-Dose Micro-CT Imaging.

Xiao Han, Junguo Bian, Diane R. Eaker, Timothy Kline, Emil Y. Sidky, Erik L. Ritman, Xiaochuan Pan

IEEE Trans. Medical Imaging, 2011

2009

Paths of Least Flow-Resistance: Characterization for the Optimization of Synthetic Tissue Scaffold Design.

Timothy Kline, Erik L. Ritman

Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA, June 28, 2009