AIMC Topic: Image Processing, Computer-Assisted

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nERdy: network analysis of endoplasmic reticulum dynamics.

Communications biology
The endoplasmic reticulum (ER) comprises smooth tubules, ribosome-studded sheets, and peripheral sheets that can present as tubular matrices. ER shaping proteins determine ER morphology, however, understanding their role in tubular matrix formation r...

BIASNN: a biologically inspired attention mechanism in spiking neural networks for image classification.

Scientific reports
Spiking Neural Networks (SNNs), designed to more accurately model the brain's neurobiological processes, have been proposed as energy-efficient alternatives to conventional Artificial Neural Networks (ANNs), which typically incur high computational a...

Hierarchical attention mechanism combined with deep neural networks for accurate semantic segmentation of dental structures in panoramic radiographs.

Scientific reports
Computer vision, a rapidly advancing branch of artificial intelligence (AI), has gained significant attention in medical and dental applications. Semantic segmentation, a key technique within computer vision, enables the precise identification and de...

Learning to Program "Recycles" Preexisting Frontoparietal Population Codes of Logical Algorithms.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Computer programming is a cornerstone of modern society, yet little is known about how the human brain enables this recently invented cultural skill. According to the neural recycling hypothesis, cultural skills (e.g., reading, math) repurpose preexi...

Distinct Portions of Superior Temporal Sulcus Combine Auditory Representations with Different Visual Streams.

The Journal of neuroscience : the official journal of the Society for Neuroscience
In humans, the superior temporal sulcus (STS) combines auditory and visual information. However, the extent to which it relies on visual information from the ventral or dorsal stream remains uncertain. To address this, we analyzed open-source functio...

Interpretable weakly-supervised learning through kernel density matrices: A digital pathology use case.

PloS one
Classification methods based on deep learning require selecting between fully-supervised or weakly-supervised approaches, each presenting limitations in uncertainty quantification and interpretability. A framework unifying both supervision modes whil...

CT radiomics-based explainable machine learning model for accurate differentiation of malignant and benign endometrial tumors: a two-center study.

Biomedical engineering online
OBJECTIVES: This study aimed to develop and validate a CT radiomics-based explainable machine learning model for precise diagnosing of malignancy and benignity specifically in endometrial cancer (EC) patients.

Training convolutional neural networks with the Forward-Forward Algorithm.

Scientific reports
Recent successes in image analysis with deep neural networks are achieved almost exclusively with Convolutional Neural Networks (CNNs), typically trained using the backpropagation (BP) algorithm. In a 2022 preprint, Geoffrey Hinton proposed the Forwa...

An interpretable crop leaf disease and pest identification model based on prototypical part network and contrastive learning.

Scientific reports
The disease and pest recognition algorithms based on computer vision can automatically process and analyze a large amount of disease and pest images, thereby achieving rapid and accurate identification of disease and pest categories on crop leaves. C...

Advanced phenotyping features utilizing deep learning techniques for automated analysis of stomatal guard cell orientation.

Scientific reports
Stomata are vital for controlling gas exchange and water vapor release, which significantly affect photosynthesis and transpiration. Characterizing stomatal traits such as size, density, and distribution is essential for adaptation to the environment...