Article

DAug: Diffusion-based Channel Augmentation for Radiology Image Retrieval and Classification

2024-12-19

Abstract excerpt

Medical image understanding requires meticulous examination of fine visual details, with particular regions requiring additional attention. While radiologists build such expertise over years of experience, it is challenging for AI models to learn where to look with limited amounts of training data. This limitation results in unsatisfying robustness in medical image understanding. To address this issue, we propose...

Topics

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

Identifiers and source

Literature Corpus work: bf4a9c19-4f6d-5b77-9f9e-e42b719bbdc9
DOI: 10.32388/8f044p

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

DAug: Diffusion-based Channel Augmentation for Radiology Image Retrieval and ClassificationDOI 10.32388/8f044pRead

MedSeg-Adapt: Clinical Query-Guided Adaptive Medical Image Segmentation via Generative Data Augmentation and BenchmarkingSemantic proximity Read

Advanced image generation for cancer using diffusion modelsSemantic proximity Read

Medical domain knowledge in domain-agnostic generative AISemantic proximity Read

Improving the generalization of Deep Learning Classification Models in Medical Imaging using Transfer Learning and Generative Adversarial NetworksSemantic proximity Read

Text-Guided Synthesis in Medical Multimedia Retrieval: A Framework for Enhanced Colonoscopy Image Classification and SegmentationSemantic proximity Read

Bridging Medical Imaging and Reports:Learning Radiologist's Nuances via Fine-Grained Multi-Modal AlignmentSemantic proximity Read

Multimodal Generative AI in Diagnostics: Bridging Medical Imaging and Clinical ReasoningSemantic proximity Read

Counterfactual MRI Generation with Denoising Diffusion Models for Interpretable Alzheimer’s Disease Effect DetectionSemantic proximity Read

A Survey of Generalization and Adaptation in Medical Imaging Foundation ModelsSemantic proximity Read

Generative Adversarial Networks (GANs) for Medical Image Synthesis and Data AugmentationSemantic proximity Read

Towards Generalist Foundation Model for RadiologySemantic proximity Read

Generative AI Enables Medical Image Segmentation in Ultra Low-Data RegimesSemantic proximity Read