AIMC Topic: Deep Learning

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High-Fidelity Permeability and Porosity Prediction Using Deep Learning With the Self-Attention Mechanism.

IEEE transactions on neural networks and learning systems
Accurate estimation of reservoir parameters (e.g., permeability and porosity) helps to understand the movement of underground fluids. However, reservoir parameters are usually expensive and time-consuming to obtain through petrophysical experiments o...

A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing Data.

IEEE transactions on neural networks and learning systems
Fiber-optic distributed acoustic sensing (DAS) is an emerging technology for vibration measurements with numerous applications in seismic signal analysis, including microseismicity detection, ambient noise tomography, earthquake source characterizati...

A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs).

PloS one
Clinical data sharing can facilitate data-driven scientific research, allowing a broader range of questions to be addressed and thereby leading to greater understanding and innovation. However, sharing biomedical data can put sensitive personal infor...

A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Dynamic contrast-enhanced (DCE) MRI commonly outperforms diffusion-weighted (DW) MRI in breast cancer discrimination. However, the side effects of contrast agents limit the use of DCE-MRI, particularly in patients with chronic kidney dise...

A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre-treatment tumor biopsies.

Medical physics
BACKGROUND: Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) has demonstrated a strong correlation to improved survival in breast cancer (BC) patients. However, pCR rates to NAC are less than 30%, depending on the BC subtype. Ea...

Predicting GPR40 Agonists with A Deep Learning-Based Ensemble Model.

ChemistryOpen
Recent studies have identified G protein-coupled receptor 40 (GPR40) as a promising target for treating type 2 diabetes mellitus, and GPR40 agonists have several superior effects over other hypoglycemic drugs, including cardiovascular protection and ...

PL-TARMI: A deep learning framework for pixel-level traffic crash risk map inference.

Accident; analysis and prevention
A citywide traffic crash risk map is of great significance for preventing future traffic crashes. However, the fine-grained geographic traffic crash risk inference is still a challenging task, mainly due to the complex road network structure, human b...

Deep Learning-Based Skin Lesion Multi-class Classification with Global Average Pooling Improvement.

Journal of digital imaging
Cancerous skin lesions are one of the deadliest diseases that have the ability in spreading across other body parts and organs. Conventionally, visual inspection and biopsy methods are widely used to detect skin cancers. However, these methods have s...

Unsupervised domain adaptation for Covid-19 classification based on balanced slice Wasserstein distance.

Computers in biology and medicine
Covid-19 has swept the world since 2020, taking millions of lives. In order to seek a rapid diagnosis of Covid-19, deep learning-based Covid-19 classification methods have been extensively developed. However, deep learning relies on many samples with...

Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images.

International journal of medical informatics
BACKGROUND: Gastrointestinal (GI) infections are quite common today around the world. Colonoscopy or wireless capsule endoscopy (WCE) are noninvasive methods for examining the whole GI tract for abnormalities. Nevertheless, it requires a great deal o...