399 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Rice UniversityUS17 papers
- The University of Texas Health Science Center at HoustonUS17 papers
- The University of Texas at AustinUS13 papers
- Harvard UniversityUS12 papers
- Baylor College of MedicineUS10 papers
- The University of Texas Southwestern Medical CenterUS9 papers
- University of PennsylvaniaUS8 papers
- Massachusetts General HospitalUS7 papers
- Stanford UniversityUS7 papers
- Technical University of MunichDE7 papers
- University of Alabama at BirminghamUS7 papers
- University of North Carolina at Chapel HillUS7 papers
17 papers · 1 filter
Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of Intelligence
Yang Li, Xin Ma, Raj Sunderraman +2
Neuroimaging-based prediction methods for intelligence and cognitive abilities have seen a rapid development in literature. Among different neuroimaging modalities, prediction base…
Brain Tumor Synthetic Data Generation with Adaptive StyleGANs
Usama Tariq, Rizwan Qureshi, Anas Zafar +5
Generative models have been very successful over the years and have received significant attention for synthetic data generation. As deep learning models are getting more and more…
Grace periods in comparative effectiveness studies of sustained treatments
Kerollos Nashat Wanis, Aaron L. Sarvet, Lan Wen +4
Researchers are often interested in estimating the effect of sustained use of a treatment on a health outcome. However, adherence to strict treatment protocols can be challenging f…
Efficient Targeted Learning of Heterogeneous Treatment Effects for Multiple Subgroups
Waverly Wei, Maya Petersen, Mark J van der Laan +3
In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogenei…
Evaluating the Performance of StyleGAN2-ADA on Medical Images
McKell Woodland, John Wood, Brian M. Anderson +12
Although generative adversarial networks (GANs) have shown promise in medical imaging, they have four main limitations that impeded their utility: computational cost, data requirem…
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)
Satrajit Chakrabarty, Syed Amaan Abidi, Mina Mousa +12
Efforts to utilize growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling owing to the data heterogeneity. Her…
Dynamic Cone-beam CT Reconstruction using Spatial and Temporal Implicit Neural Representation Learning (STINR)
You Zhang, Tielige Mengke
Objective: Dynamic cone-beam CT (CBCT) imaging is highly desired in image-guided radiation therapy to provide volumetric images with high spatial and temporal resolutions to enable…
Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans
Mary P. Gronberg, Beth M. Beadle, Adam S. Garden +20
Purpose: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. Methods: A total of 245 VMAT HN plans…
Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images
Alessia De Biase, Nanna Maria Sijtsema, Lisanne van Dijk +2
Tumor segmentation is a fundamental step for radiotherapy treatment planning. To define an accurate segmentation of the primary tumor (GTVp) of oropharyngeal cancer patients (OPC),…
Knowledge Distillation Decision Tree for Unravelling Black-box Machine Learning Models
Xuetao Lu, J. Jack Lee
Machine learning models, particularly the black-box models, are widely favored for their outstanding predictive capabilities. However, they often face scrutiny and criticism due to…
Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer
Chalapathi Charan Gajjela, Matthew Brun, Rupali Mankar +8
Mid-infrared spectroscopic imaging (MIRSI) is an emerging class of label-free techniques being leveraged for digital histopathology. Modern histopathologic identification of ovaria…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
Automation of Radiation Treatment Planning for Rectal Cancer
Kai Huang, Prajnan Das, Adenike M. Olanrewaju +9
To develop an automated workflow for rectal cancer three-dimensional conformal radiotherapy treatment planning that combines deep-learning(DL) aperture predictions and forward-plan…
Diffusion-induced anisotropic cancer invasion: a novel experimental method based on tumour spheroids
Rosalia Ferraro, Flora Ascione, Prashant Dogra +3
Tumour invasion is strongly influenced by microenvironment and, among other parameters, chemical stimuli play an important role. An innovative methodology for the quantitative inve…
OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines
Aaron Babier, Rafid Mahmood, Binghao Zhang +56
We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients w…
The effect of non-ionizing excitations on the diffusion of ion species and inter-track correlations in FLASH ultra-high dose rate radiotherapy
Ramin Abolfath, Alexander Baikalov, Stefan Bartzsch +2
We present a microscopic mechanism that accounts for the outward burst of "cold" ion species (IS) in a high-energy particle track due to coupling with ''hot" non-ion species (NIS).…
A new Standard DNA damage (SDD) data format
J. Schuemann, A. McNamara, J. W. Warmenhoven +48
Our understanding of radiation induced cellular damage has greatly improved over the past decades. Despite this progress, there are still many obstacles to fully understanding how…