Authors: Theodore Higgins Arsenault, Kenneth W. Gregg, Beatriz Guevara, Lauren E Henke, Angela Jia, Rojano Kashani, Kyle O'Carroll, Alex T. Price, Adithya Reddy, Atefeh Rezaei, Daniel E Spratt, Runyon C. Woods
Affiliation: University Hospitals Seidman Cancer Center
Abstract Preview: Purpose: To evaluate the effect of unedited AI-generated contours used for online adaptive radiotherapy (FLOW-ART) on the plan quality of prostate treatments as compared to non-adaptive (non-ART) proc...
Authors: Jenghwa Chang, Kuan Huang, Lyu Huang, Jason Lima, Jian Liu, Farzin Motamedi
Affiliation: Northwell, Department of Computer Science and Technology, Kean University, Physics and Astronomy, Hofstra University, Hofstra University Medical Physics Program
Abstract Preview: Title: A Deep Learning Approach to the Prediction of Gamma Passing Rates in VMAT Radiotherapy Plans for Adaptive Treatment.
Purpose: This study aims to develop a deep learning algorithm to predict ...
Authors: Suman Gautam, Tianjun Ma, William Song
Affiliation: Virginia Commonwealth University
Abstract Preview: Purpose: We propose an artificial intelligence (AI)-based method to rapidly predict the patient-speci๏ฌc quality assurance (PSQA) results for magnetic resonance (MR)-guided online adaptive radiation th...
Authors: Jiayi Chen, Manju Liu, Ning Wen, Haoran Zhang, Yibin Zhang
Affiliation: Department of Radiation Oncology, Ruijin Hospital, Department of Radiology, Ruijin Hospital Shanghai Jiaotong University School of Medicine, Duke Kunshan University, Department of Radiation Oncology,Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Abstract Preview: Purpose: This study introduces a novel Dual Energy CT (DECT)-Guided Intelligent Radiation Therapy (DEIT) platform designed to streamline and optimize the radiotherapy process. The DEIT system combines...
Authors: Jie Deng, Xiaoxue Qian, Hua-Chieh Shao, You Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose: Based on a 3D pre-treatment MRI scan, we developed DREME-MR to jointly reconstruct the reference patient anatomy and a data-driven, patient-specific cardiorespiratory motion model. Via a moti...
Authors: Liqin HU, Tao He, Jing JIA, Pengcheng LONG, Wei Meng, Yang Yuan
Affiliation: SuperAccuracy Science & Technology Co. Ltd.
Abstract Preview: Purpose: A multi-criteria optimization method based on reinforcement learning and adaptive boosting(RLAB MCO) has been developed to enhance radiotherapy plan quality by offering reasonable and effecti...
Authors: Chuan He, Anh H. Le, Iris Z. Wang
Affiliation: Roswell Park Comprehensive Cancer Center, Cedars-Sinai
Abstract Preview: Purpose: To develop a non-measured and DVH-based (NMDB) IMRT QA framework integrating machine learning (ML) to classify lung SBRT VMAT plans prone to delivery errors
Methods: 560 Eclipse AcurosXB l...
Authors: Hilary P Bagshaw, Mark K Buyyounouski, Cynthia Fu-Yu Chuang, Yu Gao, Dimitre Hristov, Lianli Liu, Lawrie Skinner, Lei Xing
Affiliation: Department of Radiation Oncology, Department of Radiation Oncology, Stanford University
Abstract Preview: Purpose:
MR-guided radiation therapy has introduced a significant leap in cancer treatment by allowing adaptive treatment. The low-field MR-guided system predominantly uses the TrueFISP sequence, w...
Authors: Kyle Cuneo, Issam M. El Naqa, Dale W. Litzenberg, Yiming Liu, Xueding Wang, Lise Wei, Wei Zhang, Jiaren Zou
Affiliation: University of Michigan, H. Lee Moffitt Cancer Center
Abstract Preview: Purpose: To quantitatively map 3D dose deposition during radiotherapy, empowering real-time adaptive radiation treatment.
Methods: The research features reconstructing dose deposition from acou...
Authors: Silambarasan Anbumani, Nicolette O'Connell, Eenas A. Omari, Amanda Pan, Eric S. Paulson, Lindsay Puckett, Monica E. Shukla, Dan Thill, Jiaofeng Xu
Affiliation: Elekta Inc, Elekta Limited, Linac House, Department of Radiation Oncology, Medical College of Wisconsin
Abstract Preview: Purpose: Accurate electron density information from on-board imaging is essential for direct dose calculations in adaptive radiotherapy (ART). This study evaluates a deep learning model for thoracic s...
Authors: Hao-Wen Cheng, Jonathan G. Li, Chihray Liu, Wen-Chih Tseng, Guanghua Yan
Affiliation: University of Florida
Abstract Preview: Purpose: This study develops and evaluates deep learning (DL) models for predicting 3D dose distributions in simultaneous integrated boost (SIB) prostate cancer treatment using the Elekta Unity MR-Lin...
Authors: Ergun E. Ahunbay, Abdul Parchur, Eric S. Paulson, Ilaria Rinaldi, Angelo Schiavi, Li Zhao
Affiliation: Sapienza University of Rome, Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, Department of Radiation Oncology, Medical College of Wisconsin
Abstract Preview: Purpose: Online adaptive replanning is often necessary in Intensity Modulated Proton Therapy (IMPT) due to the sensitivity of proton dose distributions to daily anatomical changes. A rapid, automated ...
Authors: Yankun Lang, Lei Ren, Dario B. Rodrigues
Affiliation: University of Maryland School of Medicine, Department of Radiation Oncology, University of Maryland School of Medicine
Abstract Preview: Purpose:
HTP of microwave (MW) phased-array systems determine MW antenna settings to maximize energy absorption (SAR in W/kg) in tumor. Conventional HTP algorithms calculate SAR based on electromag...
Authors: Evan Calabrese, Hangjie Ji, Kyle J. Lafata, Casey Y. Lee, Eugene Vaios, Chunhao Wang, Lana Wang, Zhenyu Yang, Jingtong Zhao
Affiliation: Duke University, Department of Radiation Oncology, Duke University, Duke Kunshan University, North Carolina State University
Abstract Preview: Purpose: To develop a biologically guided deep learning (DL) model for predicting brain metastasis(BM) local control outcomes following stereotactic radiosurgery (SRS). By integrating pre-SRS MR image...
Authors: Xiaoda Cong, Rohan Deraniyagala, Xuanfeng Ding, Xiaoqiang Li, Jian Liang, Peilin Liu, Craig Stevens, Xiangkun Xu, Weili Zheng
Affiliation: Corewell Health William Beaumont University Hospital, Corewellhealth William Beaumont University Hospital, William Beaumont University Hospital, Corewellhealth William Beaumont Hospital, Department of Radiation Oncology, Corewell Health William Beaumont University Hospital
Abstract Preview: Purpose:
Commission a step-and-shoot arc therapy(SPArc-step&shoot) for treating head-neck cancer patients as a desired interim milestone toward full dynamic treatment.
Methods:
An in-house de...
Authors: Mark Bowers, Gabriel Carrizo, Jimmy Caudell, Vladimir Feygelman, Kevin Greco, Christian Hahn, Jihye Koo, Kujtim Latifi, Fredrik Lofman, Jacopo Parvizi, Muqeem Qayyum, Caleb Sawyer
Affiliation: RaySearch Laboratories, Moffitt Cancer Center
Abstract Preview: Purpose: Head and neck (H&N) radiotherapy planning is complex, with multiple competing objectives. We endeavored to improve efficiency of planning by developing a deep learning (DL) model trained to p...
Authors: Sang Hee Ahn, Nalee Kim, Do Hoon Lim
Affiliation: Samsung Medical Center, Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine
Abstract Preview: Purpose: MRI offers superior soft-tissue contrast, aiding tumor localization and segmentation in radiation therapy, which traditionally relies on oncologists' expertise. This study compares CNN-based ...
Authors: Alexander Bookbinder, Matthew Tivnan, Xiangyi Wu, Wei Zhao
Affiliation: Stony Brook Medicine, Massachusetts General Hospital
Abstract Preview: Purpose: To investigate and benchmark a system-adaptive diffusion-based digital breast tomosynthesis (DBT) denoising model for a direct-indirect dual-layer flat panel detector (DI-DLFPD) with a k-edge...
Authors: Penghao Gao, Zejun Jiang, Huazhong Shu, Linlin Wang, Gongsen Zhang, Jian Zhu
Affiliation: Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration, Southeast University, Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Artificial Intelligence Laboratory, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences
Abstract Preview: Purpose: We propose a cascading framework for time-varying anatomical volumetric reconstruction and tumor-tracking, guided by onboard orthogonal-view X-ray projections.
Methods: We employe multiple...
Authors: Christopher G. Ainsley, Pradeep Bhetwal, Yingxuan Chen, Wookjin Choi, Vimal K. Desai, Karen E. Mooney, Adam Mueller, Hamidreza Nourzadeh, Yevgeniy Vinogradskiy, Maria Werner-Wasik
Affiliation: Thomas Jefferson University
Abstract Preview: Purpose: MR-guided adaptive radiotherapy (MRgART) has demonstrated improved outcomes for patients with pancreatic cancer. However, the time-consuming re-segmentation of targets and organs-at-risk (OAR...
Authors: Asma Amjad, Renae Conlin, Eric S. Paulson, Christina M. Sarosiek
Affiliation: Department of Radiation Oncology, Medical College of Wisconsin
Abstract Preview: Purpose: In an effort to improve contouring accuracy for abdominal MR guided online adaptive radiotherapy (MRgOART), patient-specific deep learning-based auto-segmentation (PS-DLAS) has been proposed....
Authors: Claus Belka, Stefanie Corradini, George Dedes, Nikolaos Delopoulos, Christopher Kurz, Guillaume Landry, Ahmad Neishabouri, Domagoj Radonic, Adrian Thummerer, Niklas Wahl, Fan Xiao
Affiliation: Department of Radiation Oncology, LMU University Hospital, LMU Munich, Department of Medical Physics, LMU Munich, Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO)
Abstract Preview: Purpose: In MR-guided online adaptive radiotherapy, MRI lacks tissue attenuation information necessary for accurate dose calculations. Instead of using deep learning methods to generate synthetic CT i...
Authors: Prabhu C. Acharya, Hassan Bagher-Ebadian, Stephen L. Brown, James R. Ewing, Mohammad M. Ghassemi, Benjamin Movsas, Farzan Siddiqui, Kundan S Thind
Affiliation: Michigan State University, Oakland University, Henry Ford Health
Abstract Preview: Purpose: Accurate T1 quantification using T One by Multiple Read Out Pulse (TOMROP) sequences is essential for physiological assessments in dynamic-contrast-enhanced (DCE) MRI and T1 mapping studies. ...
Authors: Zachary Buchwald, Chih-Wei Chang, Zach Eidex, Hui Mao, Richard L.J. Qiu, Justin R. Roper, Mojtaba Safari, Hui-Kuo Shu, Xiaofeng Yang, David Yu
Affiliation: Emory University and Winship Cancer Institute, Emory University, Department of Radiation Oncology and Winship Cancer Institute, Emory University, Emory University School of Medicine
Abstract Preview: Purpose: MRI-guided radiation therapy (MRgRT) benefits significantly from enhanced soft-tissue contrast and spatial resolution, which aid in accurately delineating tumors and organs at risk. Although ...
Authors: Enhui Chang, Yunfei Dong, Yifei Hao, Chengliang Jin, Shengsheng Lai, Yi Long, Mengni Wu, Yulu Wu, Ruimeng Yang, Zhenyu Yang, Yue Yuan, Lei Zhang, Wanli Zhang, Yaogong Zhang
Affiliation: Duke Kunshan University, Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Medical Physics Graduate Program, Duke Kunshan University
Abstract Preview: Purpose: Macrotrabecular-Massive Hepatocellular Carcinoma (MTM-HCC) is one type of liver cancer showed minimum image signature for accurate non-invasive diagnosis. This study aims to develop and evalu...
Authors: So Hyun Ahn, Chris Beltran, Byongsu Choi, Jeong Heon Kim, Jin Sung Kim, Bo Lu, Justin Chunjoo Park, Bongyong Song, Jun Tan
Affiliation: Mayo Clinic, Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Ewha Medical Research Institute, Ewha Womans University College of Medicine, UC San Diego, Yonsei University College of Medicine
Abstract Preview: Purpose:
Cone-beam computed tomography (CBCT) is widely used in IGRT for patient positioning but suffers from low resolution and poor soft tissue contrast. Synthetic CT (sCT) generated from CBCT ad...
Authors: Mark Ashamalla, Renee Farrell, Jinkoo Kim, Kartik Mani, Xin Qian, Samuel Ryu, Yizhou Zhao
Affiliation: Stony Brook Medicine, Stony Brook University Hospital
Abstract Preview: Purpose: Adaptive planning is increasingly used in head and neck radiation therapy due to factors like tumor response or changes in patient anatomy. However, methods such as resimulation or offline re...
Authors: Seungryong Cho, Donghyeok Choi, Joonil Hwang, Byung-Hee Kang, Jin Sung Kim, Eungman Lee, Younghee Park
Affiliation: Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, KAIST, Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Ewha Womans University of Medicine
Abstract Preview: Purpose: Radiation therapy (RT) is critical for cancer treatment, but changes in tumor size and shape during therapy challenge precise dose delivery. Adaptive radiation therapy (ART) addresses these v...
Authors: Asma Amjad, Renae Conlin, Eric S. Paulson, Christina M. Sarosiek
Affiliation: Department of Radiation Oncology, Medical College of Wisconsin
Abstract Preview: Purpose:
MR-guided adaptive radiation therapy (MRgART) is transforming clinical workflows, requiring fast, accurate organs-at-risk (OARs) contouring. While deep learning auto-segmentation (DLAS) of...
Authors: Huang Chi-Shiuan, Wu Chih-Chun, Hui-Yu Cathy Tsai, Chen Yan-Han, Chen Yi-Wei, Pan Yi-Ying
Affiliation: Institute of Nuclear Engineering and Science, National Tsing Hua University, Taipei Veterans General Hospital, Tri-Service General Hospital
Abstract Preview: Purpose:
This study aims to develop and validate a machine learning (ML) model based on MRI-derived radiomic features to predict progressive disease (PD) in glioblastoma (GBM) patients four months ...
Authors: Gregory Bolard, Rabten Datsang, Sarah Ghandour, Timo Kiljunen, Pauliina Paavilainen, Sami Suilamo, Katlin Tiigi
Affiliation: Turku University Hospital, Virginia Commonwealth University, MVision AI, North Estonia Medical Centre, Docrates Cancer Center, Hopital Riviera-Chablais
Abstract Preview: Purpose: To verify the performance of a vendor-neutral deep learning model for synthetic CT generation from T2-weighted and balanced steady-state MR sequences to support both MR-only simulation and MR...
Authors: David P. Adam, William T. Hrinivich, Taoran Li, Alexander Lu, Michael Salerno, Alejandro Sisniega, Boon-Keng Kevin Teo
Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Johns Hopkins University, University of Pennsylvania
Abstract Preview: Purpose: Cone beam CT (CBCT)-guided online adaptive radiotherapy (ART) is of growing interest, with recent improvements in image quality provided through larger detector panels and fast gantry rotatio...
Authors: Malvern Madondo, Mark McDonald, Zhen Tian, Christopher Valdes, Ralph Weichselbaum, Xiaofeng Yang, David Yu, Jun Zhou
Affiliation: Department of Radiation & Cellular Oncology, University of Chicago, University of Chicago, Emory University, Department of Radiology, University of Chicago, Department of Radiation Oncology and Winship Cancer Institute, Emory University
Abstract Preview: Purpose: Head-and-neck (HN) cancer patients often experience significant anatomical changes during treatment course. Proton therapy, particularly intensity-modulated proton therapy (IMPT), is sensitiv...
Authors: Wouter Crijns, Frederik Maes, Loes Vandenbroucke, Liesbeth Vandewinckele
Affiliation: Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven; Department of Radiation Oncology, UZ Leuven, Department ESAT/PSI, KU Leuven; Medical Imaging Research Center, UZ Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven
Abstract Preview: Purpose: To explore intentional deep overfit learning (IDOL) to exploit the initial treatment plan to predict an adaptive radiotherapy plan.
Methods: A conditional generative adversarial network is...
Authors: Leslie Harrell, Sanjay Maraboyina, Romy Megahed, Maida Ranjbar, Xenia Ray, Pouya Sabouri
Affiliation: Department of Radiation Oncology, University of Arkansas for Medical Sciences (UAMS), University of California San Diego
Abstract Preview: Purpose: Real-time adaptive radiation therapy (ART) dynamically modifies patientsโ treatment plan during delivery to account for anatomical and physiological variations. Addressing ART planning time v...
Authors: Bowen Jing, Jing Wang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose: Medical images acquired at multiple time points during neoadjuvant chemotherapy allow physicians to assess patientsโ responses and personalize treatment plans accordingly. Studies from the I-...
Authors: Hua-Chieh Shao, You Zhang, Ruizhi Zuo
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose: Cone-beam CT (CBCT) provides on-board patient anatomy for image guidance and treatment adaptation in radiotherapy. However, to compensate for respiration-induced anatomical motion, motion-res...
Authors: Steve B. Jiang, Dan Nguyen, Chenyang Shen, Fan-Chi F. Su, Jiacheng Xie, Shunyu Yan, Daniel Yang, Ying Zhang, You Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, The University of Texas at Dallas
Abstract Preview: Purpose: Fast dose verification is essential for the safety and efficiency of MR-guided adaptive radiotherapy (ART) as patients anxiously waiting on the treatment couch. Conventional tools often requi...
Authors: Rafe A. McBeth, Kuancheng Wang, Ledi Wang
Affiliation: Department of Radiation Oncology, University of Pennsylvania, Georgia Institute of Technology, University of Pennsylvania
Abstract Preview: Purpose:
The integration of AI in clinical workflows presents unprecedented opportunities to enhance treatment quality in radiation oncology, yet it also demands innovative approaches to address th...
Authors: Lando S. Bosma, Victoria Brennan, Nicolas Cote, ChengCheng Gui, Nima Hassan Rezaeian, Jue Jiang, Sudharsan Madhavan, Josiah Simeth, Neelam Tyagi, Harini Veeraraghavan, Michael J Zelefsky
Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, NYU Langone Health, University Medical Center Utrecht, Memorial Sloan Kettering Cancer Center
Abstract Preview: Purpose: Deep learning-based deformable image registration (DIR) models often lack robustness when applied to datasets with differing imaging characteristics. We aimed to (1) improve registration netw...
Authors: Hua-Chieh Shao, Shanshan Tang, Jing Wang, Kai Wang, You Zhang
Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Department of Radiation Oncology, University of Maryland Medical Center
Abstract Preview: Purpose: Artifacts caused by gas bubble movement in the gastrointestinal tract can severely degrade the image quality of on-board abdominal cone-beam computed tomography (CBCT), impacting its utility ...
Authors: Kim Creach, Kim Howard, Julius G. Ojwang, Richard A. Shaw, Neelu Soni
Affiliation: Mercy Hospital Springfield
Abstract Preview: Purpose: To present a standardized MRI-CT hybrid workflow for High-Dose-Rate (HDR) Image-Guided Adaptive Brachytherapy (IGBT) in cervical cancer, aligned with AAPM TG-303, as a model to assist with im...
Authors: Gong Vincent Hao, Daisuke Kawahara, Jokichi Kawazoe, Yuji Murakami, Ikuno Nishibuchi, Peiying Colleen Ruan, Daguang Xu, Dong Yang
Affiliation: Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Graduate School of Biomedical and Health Sciences, Hiroshima University, NVIDIA
Abstract Preview: Purpose:
Accurate tumor segmentation in head and neck cancer is critical for effective treatment planning, but variability in practices across medical facilities poses challenges for standardizatio...
Authors: Michael Cummings, Olga M. Dona Lemus, Hana Mekdash, Tyler Moran, Alexander R Podgorsak, Sean M. Tanny, Matthew J. Webster, Lexiang Yang, Dandan Zheng, Yuwei Zhou, Xiaofeng Zhu
Affiliation: Department of Radiation Oncology, University of Rochester, University of Miami, Inova Schar Cancer Institute, University of Rochester
Abstract Preview: Purpose: oART is revolutionizing radiotherapy by allowing treatment plans to be adjusted based on daily imaging, improving targeting precision and potentially enhancing patient outcomes. However, its ...
Authors: Stephen R. Bowen, Shijun Chen, Chunyan Duan, Daniel S. Hippe, Qiantuo Liu, Qianqian Tong, Jiajie Wang, Shouyi Wang, Faisal Yaseen
Affiliation: The University of Texas at Austin, Tongji University, University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Fred Hutchinson Cancer Center, University of Texas at Arlington
Abstract Preview: Purpose: Tumor subregion clustering and prediction of region-specific response can augment assessments and adaptive treatment decisions. A modeling framework was constructed to predict chemoradiation ...
Authors: Jie Deng, Yunxiang Li, You Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose: Magnetic Resonance Imaging (MRI) has exceptional soft tissue contrast and an essential role in radiotherapy. The introduction of clinical MR-LINACs has enabled adaptive radiotherapy (ART) usi...
Authors: Mark Anastasio, Zong Fan, Hua Li, Changjie Lu, Lulu Sun, Xiaowei Wang, Zhimin Wang, Michael Wu
Affiliation: University of Illinois at Urbana-Champaign, University of Illinois at Chicago, Washington University School of Medicine, University of Illinois Urbana-Champaign, Washington University in St. Louis, University Laboratory High School
Abstract Preview: Purpose: Histological whole slide images (WSIs) are vital in clinical diagnosis. Although deep learning (DL) methods have achieved great success in this task, they often lack interpretability. Traditi...