AIMC Topic: Deep Learning

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Deep structured learning with vision intelligence for oral carcinoma lesion segmentation and classification using medical imaging.

Scientific reports
Oral carcinoma (OC) is a toxic illness among the most general malignant cancers globally, and it has developed a gradually significant public health concern in emerging and low-to-middle-income states. Late diagnosis, high incidence, and inadequate t...

Optimizing Bi-LSTM networks for improved lung cancer detection accuracy.

PloS one
Lung cancer remains a leading cause of cancer-related deaths worldwide, with low survival rates often attributed to late-stage diagnosis. To address this critical health challenge, researchers have developed computer-aided diagnosis (CAD) systems tha...

Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diagnosis and management.

Biomedical engineering online
This paper reviews the recent advancements in the application of deep learning combined with electrocardiography (ECG) within the domain of cardiovascular diseases, systematically examining 198 high-quality publications. Through meticulous categoriza...

Deep learning algorithms for detecting fractured instruments in root canals.

BMC oral health
BACKGROUND: Identifying fractured endodontic instruments (FEIs) in periapical radiographs (PAs) is a critical yet challenging aspect of root canal treatment (RCT) due to anatomical complexities and overlapping structures. Deep learning (DL) models of...

Enhancing Functional Protein Design Using Heuristic Optimization and Deep Learning for Anti-Inflammatory and Gene Therapy Applications.

Proteins
Protein sequence design is a highly challenging task, aimed at discovering new proteins that are more functional and producible under laboratory conditions than their natural counterparts. Deep learning-based approaches developed to address this prob...

Replica tree-based federated learning using limited data.

Neural networks : the official journal of the International Neural Network Society
Learning from limited data has been extensively studied in machine learning, considering that deep neural networks achieve optimal performance when trained using a large amount of samples. Although various strategies have been proposed for centralize...

Adversarial perturbation and defense for generalizable person re-identification.

Neural networks : the official journal of the International Neural Network Society
In the Domain Generalizable Person Re-Identification (DG Re-ID) task, the quality of identity-relevant descriptor is crucial for domain generalization performance. However, for hard-matching samples, it is difficult to separate high-quality identity-...

CNN-Transformer and Channel-Spatial Attention based network for hyperspectral image classification with few samples.

Neural networks : the official journal of the International Neural Network Society
Hyperspectral image classification is an important foundational technology in the field of Earth observation and remote sensing. In recent years, deep learning has achieved a series of remarkable achievements in this area. These deep learning-based h...

AI-MET: A deep learning-based clinical decision support system for distinguishing multisystem inflammatory syndrome in children from endemic typhus.

Computers in biology and medicine
The COVID-19 pandemic brought several diagnostic challenges, including the post-infectious sequelae multisystem inflammatory syndrome in children (MIS-C). Some of the clinical features of this syndrome can be found in other pathologies such as Kawasa...

Deep Learning-Based Tumor Segmentation of Murine Magnetic Resonance Images of Prostate Cancer Patient-Derived Xenografts.

Tomography (Ann Arbor, Mich.)
BACKGROUND/OBJECTIVE: Longitudinal in vivo studies of murine xenograft models are widely utilized in oncology to study cancer biology and develop therapies. Magnetic resonance imaging (MRI) of these tumors is an invaluable tool for monitoring tumor g...