IEEE transactions on biomedical circuits and systems
Apr 2, 2025
This work presents the development of on-chip machine learning (ML) classifiers for implantable neuromodulation system-on-chips (SoCs), aimed at detecting epileptic seizures for closed-loop neuromodulation applications. Tree-based classifiers have ga...
IEEE transactions on biomedical circuits and systems
Apr 2, 2025
Associative memory is a cornerstone of cognitive intelligence within the human brain. The Bayesian confidence propagation neural network (BCPNN), a cortex-inspired model with high biological plausibility, has proven effective in emulating high-level ...
IEEE transactions on biomedical circuits and systems
Apr 2, 2025
The spiking neural network (SNN) training with spike timing-dependent plasticity (STDP) for image classification usually requires a lot of neurons to extract representative features and(or) needs an external classifier. Conventional bio-inspired lear...
IEEE transactions on biomedical circuits and systems
Apr 2, 2025
In recent years, The combination of Attention mechanism and deep learning has a wide range of applications in the field of medical imaging. However, due to its complex computational processes, existing hardware architectures have high resource consum...
Neural networks : the official journal of the International Neural Network Society
Apr 1, 2025
The objective of Domain Generalization (DG) is to devise algorithms capable of achieving high performance on previously unseen test distributions. In the pursuit of this objective, average measure has been employed as the prevalent measure for compar...
Neural networks : the official journal of the International Neural Network Society
Apr 1, 2025
As an efficient model compression method, recent knowledge distillation methods primarily transfer the knowledge from a large teacher model to a small student model by minimizing the differences between the predictions from teacher and student. Howev...
Multiple instance learning (MIL) has been successfully applied for whole slide images (WSIs) analysis in computational pathology, enabling a wide range of prediction tasks from tumor subtyping to inferring genetic mutations and multi-omics biomarkers...
Unsupervised anomaly detection (UAD) is crucial in low-dose computed tomography (LDCT). Recent AI technologies, leveraging global features, have enabled effective UAD with minimal training data of normal patients. However, this approach, devoid of ut...
The precise and efficient design of potential drug molecules with diverse physicochemical properties has long been a critical challenge. In recent years, the emergence of various deep learning-based de novo molecular generation algorithms offered new...
OBJECTIVE: Medical concept normalization (MCN) aims to map informal medical terms to formal medical concepts, a critical task in building machine learning systems for medical applications. However, most existing studies on MCN primarily focus on mode...
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