AIMC Topic: Deep Learning

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A deep transfer learning based convolution neural network framework for air temperature classification using human clothing images.

Scientific reports
Weather recognition is crucial due to its significant impact on various aspects of daily life, such as weather prediction, environmental monitoring, tourism, and energy production. Several studies have already conducted research on image-based weathe...

Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness.

Scientific reports
Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clu...

Unsupervised inter-domain transformation for virtually stained high-resolution mid-infrared photoacoustic microscopy using explainable deep learning.

Nature communications
Mid-infrared photoacoustic microscopy can capture biochemical information without staining. However, the long mid-infrared optical wavelengths make the spatial resolution of photoacoustic microscopy significantly poorer than that of conventional conf...

Pattern memory cannot be completely and truly realized in deep neural networks.

Scientific reports
The unknown boundary issue, between superior computational capability of deep neural networks (DNNs) and human cognitive ability, has becoming crucial and foundational theoretical problem in AI evolution. Undoubtedly, DNN-empowered AI capability is i...

Deep neural networks and humans both benefit from compositional language structure.

Nature communications
Deep neural networks drive the success of natural language processing. A fundamental property of language is its compositional structure, allowing humans to systematically produce forms for new meanings. For humans, languages with more compositional ...

Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features.

Radiation oncology (London, England)
BACKGROUND AND PURPOSE: Timely identification of local failure after stereotactic radiotherapy for brain metastases allows for treatment modifications, potentially improving outcomes. While previous studies showed that adding radiomics or Deep Learni...

Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors.

Alzheimer's research & therapy
BACKGROUND: Dementia is a major public health challenge in modern society. Early detection of high-risk dementia patients and timely intervention or treatment are of significant clinical importance. Neural network survival analysis represents the mos...

Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosis.

BMC medical informatics and decision making
Major underlying health issues can be indicated by even minor nail infections. Subungual Melanoma is one of the most severe kinds since it is identified at a much later stage than other conditions. The purpose of this research is to offer novel deep-...

Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction.

PLoS computational biology
The 3D structure of RNA critically influences its functionality, and understanding this structure is vital for deciphering RNA biology. Experimental methods for determining RNA structures are labour-intensive, expensive, and time-consuming. Computati...