9 accepted papers
Large language models (LLMs) have shown remarkable capabilities in language understanding and generation. However, such impressive capability typically comes with a substantial model size, which presents significant challenges in deployment and inference. While structured pruning of model parameters…
Universal medical image segmentation models have emerged as a promising paradigm due to their strong generalizability across diverse tasks, showing great potential for a wide range of clinical applications. This potential has been partly driven by the success of general-purpose vision models such as
In-context learning (ICL) offers a promising paradigm for universal medical image analysis, enabling models to perform diverse image processing tasks without retraining. However, current ICL models for medical imaging remain limited in two critical aspects: they cannot simultaneously achieve high-fi
Mixture-of-Experts (MoE) models achieve efficient scaling through sparse expert activation, but often suffer from suboptimal routing decisions due to distribution shifts in deployment. While existing test-time adaptation methods could potentially address these issues, they primarily focus on dense m…
In-context learning (ICL), a type of universal model, demonstrates exceptional generalization across a wide range of tasks without retraining by leveraging task-specific guidance from context, making it particularly effective for the intricate demands of neuroimaging. However, current ICL models, li…
In practical medical image segmentation tasks, ensuring confidence calibration is crucial. However, medical image segmentation typically relies on hard labels (one-hot vectors), and when minimizing the cross-entropy loss, the model’s softmax predictions are compelled to align with hard labels, resul…
Recent advancements in fMRI-based brain disorder diagnosis have shown that graph neural networks (GNNs) have been state-of-the-art methods for brain network analysis. Among them, transductive and inductive learning can be exploited by GNN. Transductive graphs, such as population graphs, take each su…
The multi-modal neuroimage study has dramatically facilitated disease diagnosis. Tensor-based methods are commonly used to represent multi-modal data as multi-dimensional arrays and usually implement matrix decomposition. These methods can be seen as a linear algebraic way for the lossy compression…
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and…