Article

HybridNet-XR: Efficient Teacher-Free Self-Supervised Learning for Autonomous Medical Diagnostic Systems in Resource-Constrained Environments

2026-03-19

Abstract excerpt

Deep learning model classification on large datasets is often limited in countries with restricted computational resources. While transfer learning can offset these limitations, standard architectures often maintain a high memory footprint. This study introduces HybridNet-XR, a memory-efficient and computationally lightweight hybrid convolutional neural network (CNN) designed to bridge the domain gap in medical ra...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: ba097221-e69b-5aad-b4ef-482114bf091a
DOI: 10.64898/2026.03.16.26348570

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

HybridNet-XR: Efficient Teacher-Free Self-Supervised Learning for Autonomous Medical Diagnostic Systems in Resource-Constrained EnvironmentsDOI 10.64898/2026.03.16.26348570Read

Optimizing Lightweight Medical AI for Chest CT Classification: A Distillation and Quantization ApproachSemantic proximity Read

Self-supervised Learning for Chest CT - Training Strategies and Effect on Downstream ApplicationsSemantic proximity Read

Advances in Deep Learning for Medical Imaging: Foundations, Evolution, and ImpactSemantic proximity Read

Pretraining Diversity and Clinical Metric Optimization Achieve State-of-the-Art Performance on ChestX-ray14Semantic proximity Read

NeuroInsight: A Revolutionary Self-Adaptive Framework for Precise Brain Tumor Classification in Medical ImagingUsing Adaptive Deep LearningSemantic proximity Read

Developing Deep Learning Models for Classifying Medical Imaging DataSemantic proximity Read

3D U-Net For Segmentation of Covid-19-Associated Pulmonary Infiltrates Using Transfer Learning: State-Of-The-Art Results on Affordable HardwareSemantic proximity Read

Do Hybrid CNN–Transformer Architectures Really Generalize? A Systematic Review for Medical ImagingSemantic proximity Read

Cethraian-Shift is a reproducible medical-imaging research project evaluating the cross-dataset reliability, calibration, label-policy sensitivity, and Grad-CAM++ stability of multi-label chest X-ray classifiers across NIH ChestX-ray14 and VinDr-CXRSemantic proximity Read

Synthetic MRI Pretraining for Medical Imaging TasksSemantic proximity Read

CerebroNet: A systematically derived explainable brain tumor classifier for resource-constrained MRI diagnosticsSemantic proximity Read

COPD: Pneumonia and Pneumothorax Detection in Chest X-rays: Vision Transfer based on Deep LearningSemantic proximity Read