AIMC Topic: Algorithms

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On neural architecture search and hyperparameter optimization: A max-flow based approach.

Neural networks : the official journal of the International Neural Network Society
Automated Machine Learning (AutoML) involves the automatic production of models for specific tasks on given datasets, which can be divided into two aspects: Neural Architecture Search (NAS) for model construction and Hyperparameter Optimization (HPO)...

A spectral filtering approach to represent exemplars for visual few-shot classification.

Neural networks : the official journal of the International Neural Network Society
Prototype is widely used to represent internal structure of category for few-shot learning, which was proposed as a simple inductive bias to address the issue of overfitting. However, for categories where prototypes do not exist or are difficult to r...

RNN-Based Full Waveform Inversion for Robust Multi-Parameter Bone Quantitative Imaging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The full waveform inversion (FWI) method plays a significant role in bone quantitative imaging. It is shown that even a small deviation in transducer positions can lead to a considerable variation in frequency-domain signals...

Ovarian Cancer Detection in Ascites Cytology with Weakly Supervised Model on Nationwide Data Set.

The American journal of pathology
Conventional ascitic fluid cytology for detecting ovarian cancer is limited by its low sensitivity. To address this issue, this multicenter study developed patch image (PI)-based fully supervised convolutional neural network (CNN) models and clusteri...

Statistical algorithms for the analysis of deleterious genetic mutations.

Bio Systems
We present algorithms for model selection and parameter estimation concerning deleterious genetic mutations. Three models are considered: single gene mutation, double cross-effect mutations or no genetic cause. Each of these models include unknown pa...

Enhancing clinical decision support with physiological waveforms - A multimodal benchmark in emergency care.

Computers in biology and medicine
BACKGROUND: AI-driven prediction algorithms have the potential to enhance emergency medicine by enabling rapid and accurate decision-making regarding patient status and potential deterioration. However, the integration of multimodal data, including r...

MSRP-TODNet: a multi-scale reinforced region wise analyser for tiny object detection.

BMC research notes
OBJECTIVE: Detecting small, faraway objects in real-time surveillance is challenging due to limited pixel representation, affecting classifier performance. Deep Learning (DL) techniques generate feature maps to enhance detection, but conventional met...

M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy.

BMC bioinformatics
Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds promise in advancing therapeutic development, optimizing treatment efficacy, and mitigating adverse eff...

Deep learning-based classification of coronary arteries and left ventricle using multimodal data for autonomous protocol selection or adjustment in angiography.

Scientific reports
Optimal selection of X-ray imaging parameters is crucial in coronary angiography and structural cardiac procedures to ensure optimal image quality and minimize radiation exposure. These anatomydependent parameters are organized into customizable orga...

Reverse engineering the control law for schooling in zebrafish using virtual reality.

Science robotics
Revealing the evolved mechanisms that give rise to collective behavior is a central objective in the study of cellular and organismal systems. In addition, understanding the algorithmic basis of social interactions in a causal and quantitative way of...