AIMC Topic: Deep Learning

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On the identification of thyroid nodules using semi-supervised deep learning.

International journal for numerical methods in biomedical engineering
Detecting malign cases from thyroid nodule examinations is crucial in healthcare particularly to improve the early detection of such cases. However, malign thyroid nodules can be extremely rare and is hard to find using the traditional rule based or ...

Deep learning-Based 3D inpainting of brain MR images.

Scientific reports
The detailed anatomical information of the brain provided by 3D magnetic resonance imaging (MRI) enables various neuroscience research. However, due to the long scan time for 3D MR images, 2D images are mainly obtained in clinical environments. The p...

An objective structural and functional reference standard in glaucoma.

Scientific reports
The current lack of consensus for diagnosing glaucoma makes it difficult to develop diagnostic tests derived from deep learning (DL) algorithms. In the present study, we propose an objective definition of glaucomatous optic neuropathy (GON) using cle...

DeepLPI: a multimodal deep learning method for predicting the interactions between lncRNAs and protein isoforms.

BMC bioinformatics
BACKGROUND: Long non-coding RNAs (lncRNAs) regulate diverse biological processes via interactions with proteins. Since the experimental methods to identify these interactions are expensive and time-consuming, many computational methods have been prop...

MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning.

BMC medical imaging
BACKGROUND: The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medi...

Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain.

eLife
We have developed an open-source software called bi-channel image registration and deep-learning segmentation (BIRDS) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image preprocess...

An antibody-free liver cancer screening approach based on nanoplasmonics biosensing chips via spectrum-based deep learning.

NanoImpact
The clinical needs of rapidly screening liver cancer in large populations have asked for a facile and low-cost point-of-care testing (POCT) method. We present a nanoplasmonics biosensing chip (NBC) that would empower antibody-free detection with simp...

QAIS-DSNN: Tumor Area Segmentation of MRI Image with Optimized Quantum Matched-Filter Technique and Deep Spiking Neural Network.

BioMed research international
Tumor segmentation in brain MRI images is a noted process that can make the tumor easier to diagnose and lead to effective radiotherapy planning. Providing and building intelligent medical systems can be considered as an aid for physicians. In many c...

Variables Influencing per Capita Production, Separate Collection, and Costs of Municipal Solid Waste in the Apulia Region (Italy): An Experience of Deep Learning.

International journal of environmental research and public health
Municipal solid waste (MSW) must be managed to reduce its impact on environmental matrices and population health as much as possible. In particular, the variables that influence the production, separate waste collection, and costs of MSW must be unde...

Deep learning-based solvability of underdetermined inverse problems in medical imaging.

Medical image analysis
Recently, with the significant developments in deep learning techniques, solving underdetermined inverse problems has become one of the major concerns in the medical imaging domain, where underdetermined problems are motivated by the willingness to p...