AIMC Topic: Deep Learning

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Advanced object detection for smart accessibility: a Yolov10 with marine predator algorithm to aid visually challenged people.

Scientific reports
A significant challenge for many visually impaired people is they cannot be entirely independent and are restricted by their vision. They face problems with such actions and object detection should be an essential feature they can rely on a regular b...

Deep learning-based automated detection and multiclass classification of soil-transmitted helminths and Schistosoma mansoni eggs in fecal smear images.

Scientific reports
In this work, we developed an automated system for the detection and classification of soil-transmitted helminths (STH) and Schistosoma (S.) mansoni eggs in microscopic images of fecal smears. We assembled an STH and S. mansoni dataset comprising ove...

Attention residual network for medical ultrasound image segmentation.

Scientific reports
Ultrasound imaging can distinctly display the morphology and structure of internal organs within the human body, enabling the examination of organs like the breast, liver, and thyroid. It can identify the locations of tumors, nodules, and other lesio...

A deep convolutional neural network-based novel class balancing for imbalance data segmentation.

Scientific reports
Retinal fundus images provide valuable insights into the human eye's interior structure and crucial features, such as blood vessels, optic disk, macula, and fovea. However, accurate segmentation of retinal blood vessels can be challenging due to imba...

Keypoint-based modeling reveals fine-grained body pose tuning in superior temporal sulcus neurons.

Nature communications
Body pose and orientation serve as vital visual signals in primate non-verbal social communication. Leveraging deep learning algorithms that extract body poses from videos of behaving monkeys, applied to a monkey avatar, we investigated neural tuning...

Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learning.

Nature communications
Shortcut learning poses a significant challenge to both the interpretability and robustness of artificial intelligence, arising from dataset biases that lead models to exploit unintended correlations, or shortcuts, which undermine performance evaluat...

Deep learning quantifies pathologists' visual patterns for whole slide image diagnosis.

Nature communications
Based on the expertise of pathologists, the pixelwise manual annotation has provided substantial support for training deep learning models of whole slide images (WSI)-assisted diagnostic. However, the collection of pixelwise annotation demands massiv...

Hybrid transfer learning and self-attention framework for robust MRI-based brain tumor classification.

Scientific reports
Brain tumors are a significant contributor to cancer-related deaths worldwide. Accurate and prompt detection is crucial to reduce mortality rates and improve patient survival prospects. Magnetic Resonance Imaging (MRI) is crucial for diagnosis, but m...

A novel feature extractor based on constrained cross network for detecting sleep state.

Scientific reports
With increasing awareness of healthy living and social pressure, more and more people have begun to pay attention to their sleep state. Most existing methods that utilize wrist-worn devices data for detection rely on heuristic algorithms or tradition...

DeepECG-Net: a hybrid transformer-based deep learning model for real-time ECG anomaly detection.

Scientific reports
Real-time Electrocardiogram (ECG) anomaly detection is critical for accurate diagnosis and timely intervention in cardiac disorders. Existing models, such as CNNs and LSTMs, often struggle with long-range dependencies, generalization across multiple ...