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Found 9 papers, 3 papers with code
Mutual Evidential Deep Learning for Medical Image Segmentation
Existing semi-supervised medical segmentation co-learning frameworks have realized that model performance can be diminished by the biases in model recognition caused by low-quality pseudo-labels.
Enhancing LLM Generation with Knowledge Hypergraph for Evidence-Based Medicine
Evidence-based medicine (EBM) plays a crucial role in the application of large language models (LLMs) in healthcare, as it provides reliable support for medical decision-making processes.
UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaboration
Next, each hospital learns a local knowledge transfer module offline, enabling the transfer of knowledge from the federated representation of overlapping patients to the enriched representation of local non-overlapping patients in a domain-adaptive manner.
Residual Feature-Reutilization Inception Network for Image Classification
Capturing feature information effectively is of great importance in the field of computer vision.
Generalized Uncertainty-Based Evidential Fusion with Hybrid Multi-Head Attention for Weak-Supervised Temporal Action Localization
Additionally, the proposed GUEF adaptively eliminates the interference of background noise by fusing snippet-level evidences to refine uncertainty measurement and select superior foreground feature information, which enables the model to concentrate on integral action instances to achieve better action localization and classification performance.
Optical Flow Estimation
Weakly-supervised Temporal Action Localization
+1
Theoretical Proof that Auto-regressive Language Models Collapse when Real-world Data is a Finite Set
Auto-regressive language models (LMs) have been widely used to generate data in data-scarce domains to train new LMs, compensating for the scarcity of real-world data.
Exploring LLM-based Data Annotation Strategies for Medical Dialogue Preference Alignment
We argue that the primary challenges in current RLAIF research for healthcare are the limitations of automated evaluation methods and the difficulties in accurately representing physician preferences.
Integrating Physician Diagnostic Logic into Large Language Models: Preference Learning from Process Feedback
The use of large language models in medical dialogue generation has garnered significant attention, with a focus on improving response quality and fluency.
A Generic Model for Swarm Intelligence and Its Validations
The modeling of emergent swarm intelligence constitutes a major challenge and it has been tackled in a number of different ways.
