ArticleNeuroinformatics2021
DeepNeuro: an open-source deep learning toolbox for neuroimaging.
Article in Neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Abnormal vascular structure and function within brain metastases is linked to pembrolizumab resistance.Neuro-oncology · 2024Trial
- Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation.Radiology. Artificial intelligence · 2024Trial
- Phase 2 study of pembrolizumab in patients with recurrent and residual high-grade meningiomas.Nature communications · 2022Trial
- Integration of nested cross-validation, automated hyperparameter optimization, high-performance computing to reduce and quantify the variance of test performance estimation of deep learning models.Computer methods and programs in biomedicine · 2025Article
- Simplatab: An Automated Machine Learning Framework for Radiomics-Based Bi-Parametric MRI Detection of Clinically Significant Prostate Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Hybrid deep learning approach for brain tumor classification using EfficientNetB0 and novel quantum genetic algorithm.PeerJ. Computer science · 2025Article
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice.The Lancet. Oncology · 2024Review
- Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting.NPJ digital medicine · 2023Review
- Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-Oncology (I3CR-WANO).JCO clinical cancer informatics · 2023Article
- Review
- Deep learning-based end-to-end scan-type classification, pre-processing, and segmentation of clinical neuro-oncology studies.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research.Physics in medicine and biology · 2022Article
- Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors.Neuro-oncology · 2022Article
- MEDAS: an open-source platform as a service to help break the walls between medicine and informatics.Neural computing & applications · 2022Article
- Multi-Institutional Assessment and Crowdsourcing Evaluation of Deep Learning for Automated Classification of Breast Density.Journal of the American College of Radiology : JACR · 2020Article
- Bevacizumab Reduces Permeability and Concurrent Temozolomide Delivery in a Subset of Patients with Recurrent Glioblastoma.Clinical cancer research : an official journal of the American Association for Cancer Research · 2020Article
- Automated brain extraction of multisequence MRI using artificial neural networks.Human brain mapping · 2019Article
- Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement.Neuro-oncology · 2019Article
- Vascular dysfunction promotes regional hypoxia after bevacizumab therapy in recurrent glioblastoma patients.Neuro-oncology advancesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Translating deep learning research from theory into clinical practice has unique challenges, specifically in the field of neuroimaging. In this paper, we present DeepNeuro, a Python-based deep learning framework that puts deep neural networks for neuroimaging into practical usage with a minimum of friction during implementation. We show how this framework can be used to design deep learning pipelines that can load and preprocess data, design and train various neural network architectures, and evaluate and visualize the results of trained networks on evaluation data. We present a way of reproducibly packaging data pre- and postprocessing functions common in the neuroimaging community, which facilitates consistent performance of networks across variable users, institutions, and scanners. We show how deep learning pipelines created with DeepNeuro can be concisely packaged into shareable Docker and Singularity containers with user-friendly command-line interfaces.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.