ReviewJournal of imaging2023
Developments in Image Processing Using Deep Learning and Reinforcement Learning.
Review in Journal of imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of artificial intelligence in detecting oral potentially malignant disorders and oral cancer using medical diagnostic imaging: a systematic review and meta-analysis.Frontiers in oral health · 2024Pooled it
- Artificial intelligence in congenital heart surgery: a scoping review and primer for surgeons.Translational pediatrics · 2026Review
- Advances in AI-based diagnosis of Alzheimer's disease using MRI: a comprehensive survey.Frontiers in medicine · 2026Review
- Smart Nanomedicines for Neurodegenerative Diseases: Empowering New Therapies with Molecular Imaging and Artificial Intelligence.Molecular diagnosis & therapy · 2026Review
- Ethical Use of Artificial Intelligence for Processing Medical Images.Journal of Korean medical science · 2025Article
- Global attention and local features using deep perceptron ensemble with vision Transformers for landscape design detection.Scientific reports · 2025Article
- Pursuing Better Representations: Balancing Discriminability and Transferability for Few-Shot Class-Incremental Learning.Journal of imaging · 2025Article
- Diagnostic accuracy differences in detecting wound maceration between humans and artificial intelligence: the role of human expertise revisited.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Parametric optimization of the slot waveguide characteristics using a machine-learning approach.Scientific reports · 2025Article
- Using the Deep Learning Algorithm to Determine the Presence of Sacroiliitis from Pelvic Radiographs.Life (Basel, Switzerland) · 2025Article
- Cardiac Repair and Regeneration via Advanced Technology: Narrative Literature Review.JMIR biomedical engineering · 2025Review
- Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues.Journal of imaging · 2025Review
- Image Processing Hardware Acceleration-A Review of Operations Involved and Current Hardware Approaches.Journal of imaging · 2024Review
- Robust PCA withJournal of imaging · 2024Article
- CSDNet: A Novel Deep Learning Framework for Improved Cataract State Detection.Diagnostics (Basel, Switzerland) · 2024Article
- DarwinGSE: Towards better image retrieval systems for intellectual property datasets.PloS one · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The growth in the volume of data generated, consumed, and stored, which is estimated to exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for society in general. In addition to being larger, datasets are increasingly complex, bringing new theoretical and computational challenges. Alongside this evolution, data science tools have exploded in popularity over the past two decades due to their myriad of applications when dealing with complex data, their high accuracy, flexible customization, and excellent adaptability. When it comes to images, data analysis presents additional challenges because as the quality of an image increases, which is desirable, so does the volume of data to be processed. Although classic machine learning (ML) techniques are still widely used in different research fields and industries, there has been great interest from the scientific community in the development of new artificial intelligence (AI) techniques. The resurgence of neural networks has boosted remarkable advances in areas such as the understanding and processing of images. In this study, we conducted a comprehensive survey regarding advances in AI design and the optimization solutions proposed to deal with image processing challenges. Despite the good results that have been achieved, there are still many challenges to face in this field of study. In this work, we discuss the main and more recent improvements, applications, and developments when targeting image processing applications, and we propose future research directions in this field of constant and fast evolution.
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.