AIMC Topic: Deep Learning

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Interpretable deep learning architecture for gastrointestinal disease detection: A Tri-stage approach with PCA and XAI.

Computers in biology and medicine
GI abnormalities significantly increase mortality rates and impose considerable strain on healthcare systems, underscoring the essential requirement for rapid detection, precise diagnosis, and efficient strategic treatment. To develop a CAD system, t...

Development of multistage crop yield estimation model using machine learning and deep learning techniques.

International journal of biometeorology
In this research paper, machine learning techniques were applied to a multivariate meteorological time series data for estimating the wheat yield of five districts of Punjab. Wheat yield data and weather parameters over 34 years were collected from t...

Development and Validation of a Deep Learning System to Differentiate HER2-Zero, HER2-Low, and HER2-Positive Breast Cancer Based on Dynamic Contrast-Enhanced MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Previous studies explored MRI-based radiomic features for differentiating between human epidermal growth factor receptor 2 (HER2)-zero, HER2-low, and HER2-positive breast cancer, but deep learning's effectiveness is uncertain.

Multi-horizon event detection for in-hospital clinical deterioration using dual-channel graph attention network.

International journal of medical informatics
OBJECTIVE: In hospitals globally, the occurrence of clinical deterioration within the hospital setting poses a significant healthcare burden. Rapid clinical intervention becomes a crucial task in such cases. In this research, we propose an end-to-end...

Learning soft tissue deformation from incremental simulations.

Medical physics
BACKGROUND: Surgical planning for orthognathic procedures demands swift and accurate biomechanical modeling of facial soft tissues. Efficient simulations are vital in the clinical pipeline, as surgeons may iterate through multiple plans. Biomechanica...

Magnetic resonance image denoising for Rician noise using a novel hybrid transformer-CNN network (HTC-net) and self-supervised pretraining.

Medical physics
BACKGROUND: Magnetic resonance imaging (MRI) is a crucial technique for both scientific research and clinical diagnosis. However, noise generated during MR data acquisition degrades image quality, particularly in hyperpolarized (HP) gas MRI. While de...

Tunable and real-time automatic interventional x-ray collimation from semi-supervised deep feature extraction.

Medical physics
BACKGROUND: The use of endovascular procedures is becoming increasingly popular across multiple clinical domains. These procedures are generally performed under image guidance using an interventional c-arm x-ray system. Radiation exposure to both pat...

DRGAT: Predicting Drug Responses Via Diffusion-Based Graph Attention Network.

Journal of computational biology : a journal of computational molecular cell biology
Accurately predicting drug response depending on a patient's genomic profile is critical for advancing personalized medicine. Deep learning approaches rise and especially the rise of graph neural networks leveraging large-scale omics datasets have be...

Deep learning dose prediction to approach Erasmus-iCycle dosimetric plan quality within seconds for instantaneous treatment planning.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Fast, high-quality deep learning (DL) prediction of patient-specific 3D dose distributions can enable instantaneous treatment planning (IP), in which the treating physician can evaluate the dose and approve the plan immediatel...