AIMC Topic: Deep Learning

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Explainable AI based cervical cancer prediction using FSAE feature engineering and H2O AutoML.

Scientific reports
Cervical cancer, predominantly caused by Human Papillomavirus (HPV) infection, remains a significant global health burden for women, contributing to elevated morbidity and mortality rates. Early and accurate prediction is critical in improving patien...

A Custom Annotated Dataset for Segmentation of Pulmonary Veins, Arteries, and Airways.

Scientific data
Accurate segmentation of pulmonary structures from computed tomography (CT) is critical for lung disease management, yet progress is hampered by a lack of large-scale, public datasets with comprehensive multi-structure annotations. To address this, w...

Explainable multi stream deep learning for fine grained camel breed classification using a Novel Arabian and Non Arabian dataset.

Scientific reports
Camels are resilient animals that play a crucial role in arid ecosystems and desert communities. However, distinguishing between visually similar camel breeds-particularly among Arabian camels-remains a challenging task. This paper introduces a novel...

DNALONGBENCH: a benchmark suite for long-range DNA prediction tasks.

Nature communications
Modeling long-range DNA dependencies is crucial for understanding genome structure and function across diverse biological contexts. However, effectively capturing these dependencies, which may span millions of base pairs in tasks such as three-dimens...

A deep learning approach for the analysis of birdsong.

eLife
Deep learning tools for behavior analysis have enabled important insights and discoveries in neuroscience. Yet, they often compromise interpretability and generalizability for performance, making quantitative comparisons across datasets difficult. We...

Next-generation antifungal peptide discovery: the synergy of artificial intelligence and omics technologies.

World journal of microbiology & biotechnology
There is a growing concern about fungal infections and antifungal resistance among fungal species, underscoring the need for finding alternative treatments. Antifungal peptides (AFPs) are interesting and promising candidates for developing novel anti...

A Dual-stage Deep Learning Framework for Breast Ultrasound Image Segmentation and Classification.

Journal of medical systems
Deep Learning methods have become a powerful tool in medical imaging, with great potential to improve diagnostic accuracy and support early disease detection. This is especially critical for breast cancer, one of the most common cancers among women, ...

Transforming microfluidics for single-cell analysis with robotics and artificial intelligence.

Lab on a chip
Single-cell analysis has advanced biomedical research by revealing cellular heterogeneity with unprecedented resolution, identifying rare subpopulations that drive disease progression and therapeutic resistance. Microfluidics is central to this advan...

Deep learning-based forest fire detection using an improved SSD algorithm with CBAM.

PloS one
Fires are characterized by their sudden onset, rapid spread, and destructive nature, often causing irreversible damage to ecosystems. To address the challenges in forest fire detection, including the varying scales and complex features of flame and s...

SpatialFusion: A Unified Model for Integrating Spatial Transcriptomics to Unveil Cell-type Distribution, Interaction, and Functional Heterogeneity in Tissue Microenvironments.

Journal of molecular biology
Recent advances in spatial transcriptomics (ST) have significantly enhanced our understanding of tissue structure and intercellular interactions. However, existing methods for spatial domain identification and cell type deconvolution still face chall...