ArticleCancers2024
Editorial: Recent Advances in Deep Learning and Medical Imaging for Cancer Treatment.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.Translational oncology · 2026Review
- An Automated Hybrid Deep Learning-based Model for Breast Cancer Detection using Mammographic ImagesCurrent medical imaging · 2026Article
- Machine learning in cancer imaging for enhanced precision in diagnosis and therapy.Discover computing · 2026Review
- Cross-platform multi-cancer histopathology classification using local-window vision transformers.Scientific reports · 2025Article
- Enhanced skin cancer classification using modified efficientNetV2L with adaptive early stopping mechanism.Scientific reports · 2025Article
- Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions.Mayo Clinic proceedings. Digital health · 2025Review
- Application of artificial intelligence in medical imaging for tumor diagnosis and treatment: a comprehensive approach.Discover oncology · 2025Review
- GBCHV an advanced deep learning anatomy aware model for accurate classification of gallbladder cancer utilizing ultrasound images.Scientific reports · 2025Article
- Article
- Insights into Personalized Care Strategies for Wilms Tumor: A Narrative Literature Review.Biomedicines · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the evolving landscape of medical imaging, the escalating need for deep-learningmethods takes center stage, offering the capability to autonomously acquire abstract datarepresentations crucial for early detection and classification for cancer treatment. Thecomplexities in handling diverse inputs, high-dimensional features, and subtle patternswithin imaging data are acknowledged as significant challenges in this technologicalpursuit. This Special Issue, "Recent Advances in Deep Learning and Medical Imagingfor Cancer Treatment", has attracted 19 high-quality articles that cover state-of-the-artapplications and technical developments of deep learning, medical imaging, automaticdetection, and classification, explainable artificial intelligence-enabled diagnosis for cancertreatment. In the ever-evolving landscape of cancer treatment, five pivotal themes haveemerged as beacons of transformative change. This editorial delves into the realms ofinnovation that are shaping the future of cancer treatment, focusing on five interconnectedthemes: use of artificial intelligence in medical imaging, applications of AI in cancerdiagnosis and treatment, addressing challenges in medical image analysis, advancementsin cancer detection techniques, and innovations in skin cancer classification.
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.