AIMC Topic: Neural Networks, Computer

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P2TC: A Lightweight Pyramid Pooling Transformer-CNN Network for Accurate 3D Whole Heart Segmentation.

IEEE journal of biomedical and health informatics
Cardiovascular disease is a leading global cause of death, requiring accurate heart segmentation for diagnosis and surgical planning. Deep learning methods have been demonstrated to achieve superior performances in cardiac structures segmentation. Ho...

Learning Sensor Sample-Reweighting for Dynamic Early-Exit Activity Recognition Via Meta Learning.

IEEE journal of biomedical and health informatics
During recent years, dynamic early-exit has provided a promising paradigm to improve the computational efficiency of deep neural networks by constructing multiple classifiers to let easy samples exit at shallow layers while avoiding redundant computa...

Modality-Aware Distillation Network for Microvascular Invasion Prediction of Hepatocellar Carcinoma From MRI Images.

IEEE transactions on bio-medical engineering
Microvascular invasion (MVI) of hepatocellular carcinoma (HCC) is a crucial histopathologic prognostic factor associated with cancer recurrence after liver transplantation or hepatectomy. Recently, clinicoradiologic characteristics are combined with ...

Discovery of Shared Latent Nonlinear Effective Connectivity for EEG-Based Depression Detection.

IEEE transactions on neural networks and learning systems
Granger causality (GC) effective connectivity (EC) calculated from electroencephalogram (EEG) signals has been widely used in mental disorder detection. However, the existing methods only take into account linear dynamics or nonlinear dynamics within...

A Forward and Backward Compatible Framework for Few-Shot Class-Incremental Pill Recognition.

IEEE transactions on neural networks and learning systems
Automatic pill recognition (APR) systems are crucial for enhancing hospital efficiency, assisting visually impaired individuals, and preventing cross-infection. However, most existing deep learning-based pill recognition systems can only perform clas...

ISGAN: Unsupervised Domain Adaptation With Improved Symmetric GAN for Cross-Modality Multi-Organ Segmentation.

IEEE journal of biomedical and health informatics
The differences between cross-modality medical images are significant, so several studies are working on unsupervised domain adaptation (UDA) segmentation, which aims to adapt a segmentation model trained on a labeled source domain to an unlabeled ta...

Scale-Aware Super-Resolution Network With Dual Affinity Learning for Lesion Segmentation From Medical Images.

IEEE transactions on neural networks and learning systems
Convolutional neural networks (CNNs) have shown remarkable progress in medical image segmentation. However, the lesion segmentation remains a challenge to state-of-the-art CNN-based algorithms due to the variance in scales and shapes. On the one hand...

A Memristive Spiking Neural Network Circuit for Bio-Inspired Navigation Based on Spatial Cognitive Mechanisms.

IEEE transactions on biomedical circuits and systems
Cognitive navigation, a high-level and crucial function for organisms' survival in nature, enables autonomous exploration and navigation within the environment. However, most existing works for bio-inspired navigation are implemented with non-neuromo...

EPOC: A 28-nm 5.3 pJ/SOP Event-Driven Parallel Neuromorphic Hardware With Neuromodulation-Based Online Learning.

IEEE transactions on biomedical circuits and systems
Bio-inspired neuromorphic hardware with learning ability is highly promising to achieve human-like intelligence, particularly in terms of high energy efficiency and strong environmental adaptability. Though many customized prototypes have demonstrate...

A Multi-Bit ECRAM-Based Analog Neuromorphic System With High-Precision Current Readout Achieving 97.3% Inference Accuracy.

IEEE transactions on biomedical circuits and systems
This article proposes an analog neuromorphic system that enhances symmetry, linearity, and endurance by using a high-precision current readout circuit for multi-bit nonvolatile electro-chemical random-access memory (ECRAM). For on-chip training and i...