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Found 7 papers, 4 papers with code
DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical Images
DynaMMo achieves this without compromising performance, offering a cost-effective solution for continual learning in medical applications.
FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image Analysis
The scarcity of well-annotated medical datasets requires leveraging transfer learning from broader datasets like ImageNet or pre-trained models like CLIP.
TiBiX: Leveraging Temporal Information for Bidirectional X-ray and Report Generation
To address this, we propose TiBiX: Leveraging Temporal information for Bidirectional X-ray and Report Generation.
MedMerge: Merging Models for Effective Transfer Learning to Medical Imaging Tasks
In this work, we propose MedMerge, a method whereby the weights of different models can be merged, and their features can be effectively utilized to boost performance on a new task.
XReal: Realistic Anatomy and Pathology-Aware X-ray Generation via Controllable Diffusion Model
Large-scale generative models have demonstrated impressive capacity in producing visually compelling images, with increasing applications in medical imaging.
Exploring the Transfer Learning Capabilities of CLIP in Domain Generalization for Diabetic Retinopathy
In this study, we investigate CLIP's transfer learning capabilities and its potential for cross-domain generalization in diabetic retinopathy (DR) classification.
PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism Diagnosis
Previous deep learning efforts have focused on improving the performance of Pulmonary Embolism(PE) diagnosis from Computed Tomography (CT) scans using Convolutional Neural Networks (CNN).
