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DeepMediX: A Deep Learning-Driven Resource-Efficient Medical Diagnosis Across the Spectrum
In the rapidly evolving landscape of medical imaging diagnostics, achieving high accuracy while preserving computational efficiency remains a formidable challenge.
Vision Through the Veil: Differential Privacy in Federated Learning for Medical Image Classification
The proliferation of deep learning applications in healthcare calls for data aggregation across various institutions, a practice often associated with significant privacy concerns.
See Through the Fog: Curriculum Learning with Progressive Occlusion in Medical Imaging
In this paper, we propose a novel curriculum learning-based approach to train deep learning models to handle occluded medical images effectively.
Transcending Grids: Point Clouds and Surface Representations Powering Neurological Processing
In healthcare, accurately classifying medical images is vital, but conventional methods often hinge on medical data with a consistent grid structure, which may restrict their overall performance.
