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Found 8 papers, 2 papers with code
Continual Learning in Medical Imaging from Theory to Practice: A Survey and Practical Analysis
Deep Learning has shown great success in reshaping medical imaging, yet it faces numerous challenges hindering widespread application.
On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networks
This work proposes a brain tumor segmentation method as part of the BraTS-GoAT challenge.
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.
Envisioning MedCLIP: A Deep Dive into Explainability for Medical Vision-Language Models
Explaining Deep Learning models is becoming increasingly important in the face of daily emerging multimodal models, particularly in safety-critical domains like medical imaging.
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.
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.
Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks
This study outlines our methodology for segmenting tumors in the context of two distinct tasks from the BraTS 2023 challenge: Adult Glioma and Pediatric Tumors.
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.
