AIMC Topic: Algorithms

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Explainable SHAP-XGBoost models for pressure injuries among patients requiring with mechanical ventilation in intensive care unit.

Scientific reports
pressure injuries are significant concern for ICU patients on mechanical ventilation. Early prediction is crucial for enhancing patient outcomes and reducing healthcare costs. This study aims to develop a predictive model using machine learning techn...

PCANN Program for Structure-Based Prediction of Protein-Protein Binding Affinity: Comparison With Other Neural-Network Predictors.

Proteins
In this communication, we introduce a new structure-based affinity predictor for protein-protein complexes. This predictor, dubbed PCANN (Protein Complex Affinity by Neural Network), uses the ESM-2 language model to encode the information about prote...

Structure information preserving domain adaptation network for fault diagnosis of Sucker Rod Pumping systems.

Neural networks : the official journal of the International Neural Network Society
Fault diagnosis is of great importance to the reliability and security of Sucker Rod Pumping (SRP) oil production system. With the development of digital oilfield, data-driven deep learning SRP fault diagnosis has become the development trend of oilf...

Intra-class progressive and adaptive self-distillation.

Neural networks : the official journal of the International Neural Network Society
In recent years, knowledge distillation (KD) has become widely used in compressing models, training compact and efficient students to reduce computational load and training time due to the increasing parameters in deep neural networks. To minimize tr...

AAPMatcher: Adaptive attention pruning matcher for accurate local feature matching.

Neural networks : the official journal of the International Neural Network Society
Local feature matching, which seeks to establish correspondences between two images, serves as a fundamental component in numerous computer vision applications, such as camera tracking and 3D mapping. Recently, Transformer has demonstrated remarkable...

Deep Guess acceleration for explainable image reconstruction in sparse-view CT.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Sparse-view Computed Tomography (CT) is an emerging protocol designed to reduce X-ray dose radiation in medical imaging. Reconstructions based on the traditional Filtered Back Projection algorithm suffer from severe artifacts due to sparse data. In c...

External Validation of a Machine Learning Model to Diagnose Kawasaki Disease.

The Journal of pediatrics
We investigated the generalizability of a machine learning model trained to predict Kawasaki disease using laboratory and clinical data. The algorithm performed with >89% accuracy at 3 children's hospitals across the United States, demonstrating its ...

Deformable image registration with strategic integration pyramid framework for brain MRI.

Magnetic resonance imaging
Medical image registration plays a crucial role in medical imaging, with a wide range of clinical applications. In this context, brain MRI registration is commonly used in clinical practice for accurate diagnosis and treatment planning. In recent yea...

Multi-modal MRI synthesis with conditional latent diffusion models for data augmentation in tumor segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Multimodality is often necessary for improving object segmentation tasks, especially in the case of multilabel tasks, such as tumor segmentation, which is crucial for clinical diagnosis and treatment planning. However, a major challenge in utilizing ...

A first explainable-AI-based workflow integrating forward-forward and backpropagation-trained networks of label-free multiphoton microscopy images to assess human biopsies of rare neuromuscular disease.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Diagnosis of rare neuromuscular diseases often relies on muscle biopsy analysis, which varies based on the evaluator's experience. Advances in deep learning show promise in improving diagnostic accuracy by identifying standa...