On the public citation graph

Optimizing Breast Cancer Detection: Integrating Few‐Shot and Transfer Learning for Enhanced Accuracy and Efficiency

https://doi.org/10.1002/ima.70033
0 signed · 44 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/ima.22399openalex-snapshot
Pre‐trained convolutional neural networks as feature extractors for diagnosis of breast cancer using histopathology
inferredcites →https://doi.org/10.1007/s11042-024-18352-3openalex-snapshot
Heterogeneous transfer learning: recent developments, applications, and challenges
inferredcites →https://doi.org/10.1016/b978-0-443-13999-4.00016-xopenalex-snapshot
Mammogram data analysis: Trends, challenges, and future directions
inferredcites →https://doi.org/10.1016/j.bspc.2023.105828openalex-snapshot
Explainable Soft Attentive EfficientNet for breast cancer classification in histopathological images
inferredcites →https://doi.org/10.1016/j.cogsys.2023.101188openalex-snapshot
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing explainability, adversarial robustness and zero-shot learning
inferredcites →https://doi.org/10.1016/j.heliyon.2020.e05652openalex-snapshot
Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities
inferredcites →https://doi.org/10.1016/j.patrec.2019.03.022openalex-snapshot
A novel deep learning based framework for the detection and classification of breast cancer using transfer learning
inferredcites →https://doi.org/10.1016/j.procs.2019.11.147openalex-snapshot
Offline Signature Verification using Deep Learning Convolutional Neural Network (CNN) Architectures GoogLeNet Inception-v1 and Inception-v3
inferredcites →https://doi.org/10.1016/j.procs.2024.04.175openalex-snapshot
Hybrid Inception Architecture with Residual Connection: Fine-tuned Inception-ResNet Deep Learning Model for Lung Inflammation Diagnosis from Chest Radiographs
inferredcites →https://doi.org/10.1038/s41591-020-01174-9openalex-snapshot
Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach
inferredcites →https://doi.org/10.1109/access.2020.2971225openalex-snapshot
Medical Image Classification Using a Light-Weighted Hybrid Neural Network Based on PCANet and DenseNet
inferredcites →https://doi.org/10.1109/access.2021.3104627openalex-snapshot
Multi-Scale Convolutional Neural Networks for Classification of Digital Mammograms With Breast Calcifications
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01227openalex-snapshot
Few-Shot Incremental Learning with Continually Evolved Classifiers
inferredcites →https://doi.org/10.1109/iccv.2015.314openalex-snapshot
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition
inferredcites →https://doi.org/10.1109/ictc58733.2023.10392830openalex-snapshot
Transfer Learning in Brain Tumor Classification: Challenges, Opportunities, and Future Prospects
inferredcites →https://doi.org/10.1109/jbhi.2016.2636665openalex-snapshot
Deep Learning for Health Informatics
inferredcites →https://doi.org/10.1109/jbhi.2022.3228577openalex-snapshot
DEBCM: Deep Learning-Based Enhanced Breast Invasive Ductal Carcinoma Classification Model in IoMT Healthcare Systems
inferredcites →https://doi.org/10.1109/tgrs.2021.3099033openalex-snapshot
SPNet: Siamese-Prototype Network for Few-Shot Remote Sensing Image Scene Classification
inferredcites →https://doi.org/10.1109/tgrs.2022.3219726openalex-snapshot
Improving Few-Shot Remote Sensing Scene Classification With Class Name Semantics
inferredcites →https://doi.org/10.1145/3386252openalex-snapshot
Generalizing from a Few Examples
inferredcites →https://doi.org/10.1145/3582688openalex-snapshot
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities
inferredcites →https://doi.org/10.1145/3735633openalex-snapshot
Continual Learning of Large Language Models: A Comprehensive Survey
inferredcites →https://doi.org/10.1155/2021/6659083openalex-snapshot
Deep Residual Network in Network
inferredcites →https://doi.org/10.1155/2022/8904768openalex-snapshot
Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning
inferredcites →https://doi.org/10.1609/aaai.v38i10.29024openalex-snapshot
Pushing the Limit of Fine-Tuning for Few-Shot Learning: Where Feature Reusing Meets Cross-Scale Attention
inferredcites →https://doi.org/10.3389/fphar.2022.929755openalex-snapshot
Semi-supervised vision transformer with adaptive token sampling for breast cancer classification
inferredcites →https://doi.org/10.3390/app10134523openalex-snapshot
Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
inferredcites →https://doi.org/10.3390/math10214109openalex-snapshot
BreaST-Net: Multi-Class Classification of Breast Cancer from Histopathological Images Using Ensemble of Swin Transformers
inferredcites →https://doi.org/10.48550/arxiv.1703.05175openalex-snapshot
Prototypical Networks for Few-shot Learning
inferredcites →https://doi.org/10.48550/arxiv.1704.04861openalex-snapshot
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
inferredcites →https://doi.org/10.48550/arxiv.2107.05037openalex-snapshot
BCNet: A Deep Convolutional Neural Network for Breast Cancer Grading
inferredcites →https://doi.org/10.7717/peerj-cs.715openalex-snapshot
Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: an experimental study

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s10791-026-10054-8openalex-snapshot
A unified framework for oral squamous cell carcinoma detection using machine learning and deep learning techniques
inferred← citeshttps://doi.org/10.1007/s42044-025-00273-5openalex-snapshot
Enhancing breast cancer classification: a few-shot meta-learning framework with DenseNet-121 for improved diagnosis
inferred← citeshttps://doi.org/10.1007/s44163-026-02189-wopenalex-snapshot
Multimodal sensor data fusion and event recognition in IoT
inferred← citeshttps://doi.org/10.1007/s44248-026-00116-zopenalex-snapshot
Advancements in deep learning for early breast cancer detection using artificial intelligence models in low resource settings
inferred← citeshttps://doi.org/10.1080/02564602.2026.2663399openalex-snapshot
Evolution of Fault-tolerant Control in Induction Motors: From Model-based to Data-driven Intelligence
inferred← citeshttps://doi.org/10.1080/21681163.2026.2626127openalex-snapshot
A Sugeno-fuzzy integral fusion framework for brain tumor identification using transfer learning models
inferred← citeshttps://doi.org/10.1109/iceconf65644.2025.11379493openalex-snapshot
AECNN: A Dual-Stage Deep Learning Framework for Accurate and Efficient Ultrasound-Based Breast Cancer Detection
inferred← citeshttps://doi.org/10.2139/ssrn.5230103openalex-snapshot
Intelligent Sustainability: Harnessing AI for a Greener Future
inferred← citeshttps://doi.org/10.2139/ssrn.5263827openalex-snapshot
Hybrid Bayesian Deep Learning for Explainable Breast Cancer Diagnosis in Telemedicine: Integrating Multi-Modal Data
inferred← citeshttps://doi.org/10.3390/app15158448openalex-snapshot
Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
inferred← citeshttps://doi.org/10.3390/app16147196openalex-snapshot
Performance Comparison of Lightweight Neural Networks for Brain Tumor Classification on Magnetic Resonance Imaging (MRI)
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.