AIMC Topic: Algorithms

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Data splitting to avoid information leakage with DataSAIL.

Nature communications
Information leakage is an increasingly important topic in machine learning research for biomedical applications. When information leakage happens during a model's training, it risks memorizing the training data instead of learning generalizable prope...

Transfer learning and data augmentation for glucose concentration prediction from colorimetric biosensor images.

Mikrochimica acta
A deep learning algorithm is introduced to accurately predict glucose concentrations using colorimetric paper sensor (CPS) images. We used an image dataset from CPS treated with five different glucose concentrations as input for deep learning models....

Rapid dose prediction for lung CyberKnife radiotherapy plans utilizing a deep learning approach by incorporating dosimetric features delivered by noncoplanar beams.

Biomedical physics & engineering express
. The dose distribution of lung cancer patients treated with the CyberKnife (CK) system is influenced by various factors, including tumor location and the direction of CK beams. The objective of this study is to present a deep learning approach that ...

Optimization of shunting operation plan in large freight train depot based on DQN algorithm.

PloS one
Shunting operation plan is the main daily work of the freight train depot, the optimization of shunting operation plan is of great significance to improve the efficiency of railway operation and production and transportation. In this paper, the deep ...

Edge-enhanced interaction graph network for protein-ligand binding affinity prediction.

PloS one
Protein-ligand interactions are crucial in drug discovery. Accurately predicting protein-ligand binding affinity is essential for screening potential drugs. Graph neural networks have proven highly effective in modeling spatial relationships and thre...

Bridging the Gap: Challenges and Strategies for the Implementation of Artificial Intelligence-based Clinical Decision Support Systems in Clinical Practice.

Yearbook of medical informatics
OBJECTIVES: Despite the surge in development of artificial intelligence (AI) algorithms to support clinical decision-making, few of these algorithms are used in practice. We reviewed recent literature on clinical deployment of AI-based clinical decis...

Event-based optical flow on neuromorphic processor: ANN vs. SNN comparison based on activation sparsification.

Neural networks : the official journal of the International Neural Network Society
Spiking neural networks (SNNs) for event-based optical flow are claimed to be computationally more efficient than their artificial neural networks (ANNs) counterparts, but a fair comparison is missing in the literature. In this work, we propose an ev...

A prompt regularization approach to enhance few-shot class-incremental learning with Two-Stage Classifier.

Neural networks : the official journal of the International Neural Network Society
With a limited number of labeled samples, Few-Shot Class-Incremental Learning (FSCIL) seeks to efficiently train and update models without forgetting previously learned tasks. Because pre-trained models can learn extensive feature representations fro...

Synthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues.

CPT: pharmacometrics & systems pharmacology
With the recent and evolving regulatory frameworks regarding the usage of Artificial Intelligence (AI) in both drug and medical device development, the differentiation between data derived from observed ('true' or 'real') sources and artificial data ...

CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling.

Genome biology
Low-pass single-cell DNA sequencing technologies and algorithmic advancements have enabled haplotype-specific copy number calling on thousands of cells within tumors. However, measurement uncertainty may result in spurious CNAs inconsistent with real...