MedSynapse-V proposes a latent diagnostic memory evolution framework using Meta Query, Causal Counterfactual Refinement, and Intrinsic Memory Transition to improve medical VLM diagnostic accuracy over chain-of-thought methods.
IEEE Transactions on Information theory37(1), 145–151 (2002)
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Similarity-constrained adversarial perturbations reduce drift signals in malware classifiers while achieving evasion, with l2 regularization performing best.
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MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution
MedSynapse-V proposes a latent diagnostic memory evolution framework using Meta Query, Causal Counterfactual Refinement, and Intrinsic Memory Transition to improve medical VLM diagnostic accuracy over chain-of-thought methods.
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Adversarial Evasion in Non-Stationary Malware Detection: Minimizing Drift Signals through Similarity-Constrained Perturbations
Similarity-constrained adversarial perturbations reduce drift signals in malware classifiers while achieving evasion, with l2 regularization performing best.