AIMC Topic: Deep Learning

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Multitask deep learning for prediction of microvascular invasion and recurrence-free survival in hepatocellular carcinoma based on MRI images.

Liver international : official journal of the International Association for the Study of the Liver
BACKGROUND AND AIMS: Accurate preoperative prediction of microvascular invasion (MVI) and recurrence-free survival (RFS) is vital for personalised hepatocellular carcinoma (HCC) management. We developed a multitask deep learning model to predict MVI ...

Advanced hybrid attention-based deep learning network with heuristic algorithm for adaptive CT and PET image fusion in lung cancer detection.

Medical engineering & physics
Lung cancer is one of the most deadly diseases in the world. Lung cancer detection can save the patient's life. Despite being the best imaging tool in the medical sector, clinicians find it challenging to interpret and detect cancer from Computed Tom...

Trustworthy clinical AI solutions: A unified review of uncertainty quantification in Deep Learning models for medical image analysis.

Artificial intelligence in medicine
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. End users are particularly reluctant to rely on the opaque predictions of DL mo...

Development and validation of an electrocardiographic artificial intelligence model for detection of peripartum cardiomyopathy.

American journal of obstetrics & gynecology MFM
BACKGROUND: This study used electrocardiogram data in conjunction with artificial intelligence methods as a noninvasive tool for detecting peripartum cardiomyopathy.

Spatial landmark detection and tissue registration with deep learning.

Nature methods
Spatial landmarks are crucial in describing histological features between samples or sites, tracking regions of interest in microscopy, and registering tissue samples within a common coordinate framework. Although other studies have explored unsuperv...

A new strategy for groundwater level prediction using a hybrid deep learning model under Ecological Water Replenishment.

Environmental science and pollution research international
Accurate prediction of the groundwater level (GWL) is crucial for sustainable groundwater resource management. Ecological water replenishment (EWR) involves artificially diverting water to replenish the ecological flow and water resources of both sur...

Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms.

Environmental science and pollution research international
Coastal aquifer vulnerability assessment (CAVA) studies are essential for mitigating the effects of seawater intrusion (SWI) worldwide. In this research, the vulnerability of the coastal aquifer in the Lahijan region of northwest Iran was investigate...

Context-dependent design of induced-fit enzymes using deep learning generates well-expressed, thermally stable and active enzymes.

Proceedings of the National Academy of Sciences of the United States of America
The potential of engineered enzymes in industrial applications is often limited by their expression levels, thermal stability, and catalytic diversity. De novo enzyme design faces challenges due to the complexity of enzymatic catalysis. An alternativ...

BREATH-Net: a novel deep learning framework for NO prediction using bi-directional encoder with transformer.

Environmental monitoring and assessment
Air pollution poses a significant challenge in numerous urban regions, negatively affecting human well-being. Nitrogen dioxide (NO) is a prevalent atmospheric pollutant that can potentially exacerbate respiratory ailments and cardiovascular disorders...