7.2k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Mayo Clinic in FloridaUS31 papers
- Mayo ClinicUS27 papers
- Arizona State UniversityUS16 papers
- WinnMedUS10 papers
- University of Illinois Urbana-ChampaignUS9 papers
- Emory UniversityUS8 papers
- University of PennsylvaniaUS8 papers
- Harvard UniversityUS7 papers
- Cornell UniversityUS6 papers
- National Institutes of HealthUS6 papers
- Yale UniversityUS6 papers
- Indiana University – Purdue University IndianapolisUS5 papers
12 papers · 1 filter
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023
Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu +77
Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%.…
A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images
Peilong Wang, Timothy L. Kline, Andy D. Missert +6
Automated segmentation tools often encounter accuracy and adaptability issues when applied to images of different pathology. The purpose of this study is to explore the feasibility…
Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge
Dominic LaBella, Ujjwal Baid, Omaditya Khanna +119
We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in…
Analysis and Validation of Image Search Engines in Histopathology
Isaiah Lahr, Saghir Alfasly, Peyman Nejat +16
Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient matching for various purposes, ranging from triaging and d…
MedYOLO: A Medical Image Object Detection Framework
Joseph Sobek, Jose R. Medina Inojosa, Betsy J. Medina Inojosa +4
Artificial intelligence-enhanced identification of organs, lesions, and other structures in medical imaging is typically done using convolutional neural networks (CNNs) designed to…
Immunohistochemistry Biomarkers-Guided Image Search for Histopathology
Abubakr Shafique, Morteza Babaie, Ricardo Gonzalez +1
Medical practitioners use a number of diagnostic tests to make a reliable diagnosis. Traditionally, Haematoxylin and Eosin (H&E) stained glass slides have been used for cancer diag…
Composite Biomarker Image for Advanced Visualization in Histopathology
Abubakr Shafique, Morteza Babaie, Ricardo Gonzalez +3
Immunohistochemistry (IHC) biomarkers are essential tools for reliable cancer diagnosis and subtyping. It requires cross-staining comparison among Whole Slide Images (WSIs) of IHCs…
Incremental Cross-Domain Adaptation for Robust Retinopathy Screening via Bayesian Deep Learning
Taimur Hassan, Bilal Hassan, Muhammad Usman Akram +3
Retinopathy represents a group of retinal diseases that, if not treated timely, can cause severe visual impairments or even blindness. Many researchers have developed autonomous sy…
The Federated Tumor Segmentation (FeTS) Challenge
Sarthak Pati, Ujjwal Baid, Maximilian Zenk +29
This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standar…
Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos
Amir Mohammad Naderi, Haisong Bu, Jingcheng Su +8
Zebrafish is a powerful and widely-used model system for a host of biological investigations including cardiovascular studies and genetic screening. Zebrafish are readily assessabl…
Predicting Future Cognitive Decline with Hyperbolic Stochastic Coding
J. Zhang, Q. Dong, J. Shi +9
Hyperbolic geometry has been successfully applied in modeling brain cortical and subcortical surfaces with general topological structures. However such approaches, similar to other…
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
Zongwei Zhou, Vatsal Sodha, Md Mahfuzur Rahman Siddiquee +4
Transfer learning from natural image to medical image has established as one of the most practical paradigms in deep learning for medical image analysis. However, to fit this parad…