Ctrl-GenAug: Controllable Generative Augmentation for Medical Sequence Classification
In the medical field, the limited availability of large-scale datasets and labor-intensive
annotation processes hinder the performance of deep models. Diffusion-based generative …
annotation processes hinder the performance of deep models. Diffusion-based generative …
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. Issues like catastrophic forgetting …
numerous challenges hindering widespread application. Issues like catastrophic forgetting …
Robust Box Prompt based SAM for Medical Image Segmentation
The Segment Anything Model (SAM) can achieve satisfactory segmentation performance
under high-quality box prompts. However, SAM's robustness is compromised by the decline …
under high-quality box prompts. However, SAM's robustness is compromised by the decline …
Non-Parametric Neighborhood Test-Time Generalization: Application to Medical Image Classification
Reliable and stable performance is crucial for the application of computer-aided medical
image systems in clinical settings. However, approaches based on deep learning often fail …
image systems in clinical settings. However, approaches based on deep learning often fail …