AIMC Topic: Algorithms

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Leveraging weak supervision for cell localization in digital pathology using multitask learning and consistency loss.

Computers in biology and medicine
Cell detection and segmentation are integral parts of automated systems in digital pathology. Encoder-decoder networks have emerged as a promising solution for these tasks. However, training of these networks has typically required full boundary anno...

Interpretable one-class classification framework for prescription error detection using BERT embeddings and dimensionality reduction.

Computers in biology and medicine
Ensuring accurate prescriptions and proper medication administration is critical for patient safety and effective clinical outcomes. Identifying and preventing prescription errors can significantly reduce healthcare costs and adverse health effects. ...

Optimization of spatio-temporal ozone (O) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm.

Ecotoxicology and environmental safety
The future of ozone (O) pollution presents significant environmental and public health challenges worldwide. High O levels can harm respiratory health, exacerbating conditions such as asthma and increasing the risk of cardiovascular diseases. Address...

Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms.

BMC anesthesiology
BACKGROUND: With the aging demographic on the rise, we're seeing a spike in the occurrence of postoperative delirium (POD). Our research aims to delve into the connection between plasma bilirubin levels and postoperative delirium, with the goal of cr...

Histopathological-based brain tumor grading using 2D-3D multi-modal CNN-transformer combined with stacking classifiers.

Scientific reports
Reliability in diagnosing and treating brain tumors depends on the accurate grading of histopathological images. However, limited scalability, adaptability, and interpretability challenge current methods for frequently grading brain tumors to accurat...

WSDC-ViT: a novel transformer network for pneumonia image classification based on windows scalable attention and dynamic rectified linear unit convolutional modules.

Scientific reports
Accurate differential diagnosis of pneumonia remains a challenging task, as different types of pneumonia require distinct treatment strategies. Early and precise diagnosis is crucial for minimizing the risk of misdiagnosis and for effectively guiding...

Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases.

Scientific reports
Globally, nervous system diseases are the leading cause of disability-adjusted life-years and the second leading cause of mortality in the world. Traditional diagnostic methods for nervous system diseases are expensive. So this study aimed to constru...

CYCLONE: recycle contrastive learning for integrating single-cell gene expression data.

BMC bioinformatics
BACKGROUND: Combining single-cell transcriptome sequencing results from several batches reduces batch effect, which improves our understanding of cellular identity and function.

Contrastive learning-driven framework for neuron morphology classification.

Scientific reports
The Neuron morphology classification is a critical task in neuroscience research, as the morphological features of neurons are closely linked to the functional characteristics of neural circuits. However, traditional classification methods often stru...

A privacy preserving machine learning framework for medical image analysis using quantized fully connected neural networks with TFHE based inference.

Scientific reports
Medical image analysis using deep learning algorithms has become a basis of modern healthcare, enabling early detection, diagnosis, treatment planning, and disease monitoring. However, sharing sensitive raw medical data with third parties for analysi...