AIMC Topic: Humans

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Assessment of deep learning-based auto-contouring on interobserver consistency in target volume and organs-at-risk delineation for breast cancer: Implications for RTQA program in a multi-institutional study.

Breast (Edinburgh, Scotland)
PURPOSE: To quantify interobserver variation (IOV) in target volume and organs-at-risk (OAR) contouring across 31 institutions in breast cancer cases and to explore the clinical utility of deep learning (DL)-based auto-contouring in reducing potentia...

Big data and artificial intelligence in cancer research.

Trends in cancer
The field of oncology has witnessed an extraordinary surge in the application of big data and artificial intelligence (AI). AI development has made multiscale and multimodal data fusion and analysis possible. A new era of extracting information from ...

Generating meaning: active inference and the scope and limits of passive AI.

Trends in cognitive sciences
Prominent accounts of sentient behavior depict brains as generative models of organismic interaction with the world, evincing intriguing similarities with current advances in generative artificial intelligence (AI). However, because they contend with...

Predicting stroke outcome: A case for multimodal deep learning methods with tabular and CT Perfusion data.

Artificial intelligence in medicine
MOTIVATION: Acute ischemic stroke is one of the leading causes of morbidity and disability worldwide, often followed by a long rehabilitation period. To improve and personalize stroke rehabilitation, it is essential to provide a reliable prognosis to...

Deep learning based histological classification of adnex tumors.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Cutaneous adnexal tumors are a diverse group of tumors arising from structures of the hair appendages. Although often benign, malignant entities occur which can metastasize and lead to patients´ death. Correct diagnosis is critical to ens...

Advances, challenges, and future research needs in machine learning-based crash prediction models: A systematic review.

Accident; analysis and prevention
Accurately modelling crashes, and predicting crash occurrence and associated severities are a prerequisite for devising countermeasures and developing effective road safety management strategies. To this end, crash prediction modelling using machine ...

Utilizing deep learning techniques to improve image quality and noise reduction in preclinical low-dose PET images in the sinogram domain.

Medical physics
BACKGROUND: Low-dose positron emission tomography (LD-PET) imaging is commonly employed in preclinical research to minimize radiation exposure to animal subjects. However, LD-PET images often exhibit poor quality and high noise levels due to the low ...

An Efficient Group Federated Learning Framework for Large-Scale EEG-Based Driver Drowsiness Detection.

International journal of neural systems
To avoid traffic accidents, monitoring the driver's electroencephalogram (EEG) signals to assess drowsiness is an effective solution. However, aggregating the personal data of these drivers may lead to insufficient data usage and pose a risk of priva...

Connecting the dots towards precision orthodontics.

Orthodontics & craniofacial research
Precision orthodontics entails the use of personalized clinical, biological, social and environmental knowledge of each patient for deep individualized clinical phenotyping and diagnosis combined with the delivery of care using advanced customized de...

Convolutional neural network-based kidney volume estimation from low-dose unenhanced computed tomography scans.

BMC medical imaging
PURPOSE: Kidney volume is important in the management of renal diseases. Unfortunately, the currently available, semi-automated kidney volume determination is time-consuming and prone to errors. Recent advances in its automation are promising but mos...