ReviewCancers2021
Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging.
Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 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
49 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China.Frontiers in digital health · 2025Pooled it
- Deep Learning-Based Method for Damage Severity Grading of Black Shell Disease in Hong Kong Oyster (Crassostrea hongkongensis) Caused by Polydora Complex.Journal of fish diseases · 2026Article
- Dual-discriminator network-based classification method for breast ultrasound imaging.Quantitative imaging in medicine and surgery · 2026Article
- Cross-cancer survival prediction using machine learning models.Scientific reports · 2026Article
- Development and evaluation of a multistage transfer learning framework for robust medical image analysis.Scientific reports · 2026Article
- Combination Ensemble and Explainable Deep Learning Framework for High-Accuracy Classification of Wild Edible Macrofungi.Biology · 2025Article
- Masked pretraining of U-Net for ultrasound image segmentation.Scientific reports · 2025Article
- Evaluation of Transfer Learning Efficacy for Surgical Suture Quality Classification on Limited Datasets.Journal of imaging · 2025Article
- When deep learning is not enough: artificial life as a supplementary tool for segmentation of ultrasound images of breast cancer.Medical & biological engineering & computing · 2025Article
- Breaking Diagnostic Barriers: Vision Transformers Redefine Monkeypox Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning model meets community-based surveillance of acute flaccid paralysis.Infectious Disease Modelling · 2025Article
- Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review.BMC medical research methodology · 2025Article
- Exploring AI Approaches for Breast Cancer Detection and Diagnosis: A Review Article.Breast cancer (Dove Medical Press) · 2025Review
- Effective SMOTE boost with deep learning for IDC identification in whole-slide images.PloS one · 2025Article
- An approach for classification of breast cancer using lightweight deep convolution neural network.Heliyon · 2024Article
- Transfer learning classification of suspicious lesions on breast ultrasound: is there room to avoid biopsies of benign lesions?European radiology experimental · 2024Article
- Concatenated CNN-Based Pneumonia Detection Using a Fuzzy-Enhanced Dataset.Sensors (Basel, Switzerland) · 2024Article
- GPT-Driven Radiology Report Generation with Fine-Tuned Llama 3.Bioengineering (Basel, Switzerland) · 2024Article
- Electrical lymph node scanning (ELS) system for real-time intra-operative detection of involved axillary lymph nodes in adjuvant breast cancer patients.Scientific reports · 2024Article
- Advanced detection of coronary artery disease via deep learning analysis of plasma cytokine data.Frontiers in cardiovascular medicine · 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
3 authors.
Funding
Abstract
Transfer learning is a machine learning approach that reuses a learning method developed for a task as the starting point for a model on a target task. The goal of transfer learning is to improve performance of target learners by transferring the knowledge contained in other (but related) source domains. As a result, the need for large numbers of target-domain data is lowered for constructing target learners. Due to this immense property, transfer learning techniques are frequently used in ultrasound breast cancer image analyses. In this review, we focus on transfer learning methods applied on ultrasound breast image classification and detection from the perspective of transfer learning approaches, pre-processing, pre-training models, and convolutional neural network (CNN) models. Finally, comparison of different works is carried out, and challenges-as well as outlooks-are discussed.
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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.