AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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Dual selective fusion transformer network for hyperspectral image classification.

Neural networks : the official journal of the International Neural Network Society
Transformer has achieved satisfactory results in the field of hyperspectral image (HSI) classification. However, existing Transformer models face two key challenges when dealing with HSI scenes characterized by diverse land cover types and rich spect...

Episodic Memory-Double Actor-Critic Twin Delayed Deep Deterministic Policy Gradient.

Neural networks : the official journal of the International Neural Network Society
Existing deep reinforcement learning (DRL) algorithms suffer from the problem of low sample efficiency. Episodic memory allows DRL algorithms to remember and use past experiences with high return, thereby improving sample efficiency. However, due to ...

Approximation by non-symmetric networks for cross-domain learning.

Neural networks : the official journal of the International Neural Network Society
For the past 30 years or so, machine learning has stimulated a great deal of research in the study of approximation capabilities (expressive power) of a multitude of processes, such as approximation by shallow or deep neural networks, radial basis fu...

ABVS breast tumour segmentation via integrating CNN with dilated sampling self-attention and feature interaction Transformer.

Neural networks : the official journal of the International Neural Network Society
Given the rapid increase in breast cancer incidence, the Automated Breast Volume Scanner (ABVS) is developed to screen breast tumours efficiently and accurately. However, reviewing ABVS images is a challenging task owing to the significant variations...

DuPt: Rehearsal-based continual learning with dual prompts.

Neural networks : the official journal of the International Neural Network Society
The rehearsal-based continual learning methods usually involve reviewing a small number of representative samples to enable the network to learn new contents while retaining old knowledge. However, existing works overlook two crucial factors: (1) Whi...

RISE-Editing: Rotation-invariant neural point fields with interactive segmentation for fine-grained and efficient editing.

Neural networks : the official journal of the International Neural Network Society
Neural Radiance Fields (NeRF) have shown great potential for synthesizing novel views. Currently, despite the existence of some initial controllable and editable NeRF methods, they remain limited in terms of efficient and fine-grained editing capabil...

Exploiting instance-label dynamics through reciprocal anchored contrastive learning for few-shot relation extraction.

Neural networks : the official journal of the International Neural Network Society
In the domain of Few-shot Relation Extraction (FSRE), the primary objective is to distill relational facts from limited labeled datasets. This task has recently witnessed significant advancements through the integration of Pre-trained Language Models...

Memory flow-controlled knowledge tracing with three stages.

Neural networks : the official journal of the International Neural Network Society
Knowledge Tracing (KT), as a pivotal technology in intelligent education systems, analyzes students' learning data to infer their knowledge acquisition and predict their future performance. Recent advancements in KT recognize the importance of memory...

Dual-view graph-of-graph representation learning with graph Transformer for graph-level anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Graph-Level Anomaly Detection (GLAD) endeavors to pinpoint a small subset of anomalous graphs that deviate from the normal data distribution within a given set of graph data. Existing GLAD methods typically rely on Graph Neural Networks (GNNs) to ext...

Quantum federated learning with pole-angle quantum local training and trainable measurement.

Neural networks : the official journal of the International Neural Network Society
Recently, quantum federated learning (QFL) has received significant attention as an innovative paradigm. QFL has remarkable features by employing quantum neural networks (QNNs) instead of conventional neural networks owing to quantum supremacy. In or...