AIMC Topic: Deep Learning

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Microscopic image recognition of diatoms based on deep learning.

Journal of phycology
Diatoms are a crucial component in the study of aquatic ecosystems and ancient environmental records. However, traditional methods for identifying diatoms, such as morphological taxonomy and molecular detection, are costly, are time consuming, and ha...

Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study.

BMC cancer
BACKGROUND: Calcification is a common phenomenon in both benign and malignant thyroid nodules. However, the clinical significance of calcification remains unclear. Therefore, we explored a more objective method for distinguishing between benign and m...

Comparison between vision transformers and convolutional neural networks to predict non-small lung cancer recurrence.

Scientific reports
Non-Small cell lung cancer (NSCLC) is one of the most dangerous cancers, with 85% of all new lung cancer diagnoses and a 30-55% of recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients during diagnosis could...

Predicting intratumoral fluid pressure and liposome accumulation using physics informed deep learning.

Scientific reports
Liposome-based anticancer agents take advantage of the increased vascular permeability and transvascular pressure gradients for selective accumulation in tumors, a phenomenon known as the enhanced permeability and retention(EPR) effect. The EPR effec...

Computer-aided diagnosis of keratoconus through VAE-augmented images using deep learning.

Scientific reports
Detecting clinical keratoconus (KCN) poses a challenging and time-consuming task. During the diagnostic process, ophthalmologists are required to review demographic and clinical ophthalmic examinations in order to make an accurate diagnosis. This stu...

DeepHLAPred: a deep learning-based method for non-classical HLA binder prediction.

BMC genomics
Human leukocyte antigen (HLA) is closely involved in regulating the human immune system. Despite great advance in detecting classical HLA Class I binders, there are few methods or toolkits for recognizing non-classical HLA Class I binders. To fill in...

Use of deep learning to segment bolus during videofluoroscopic swallow studies.

Biomedical physics & engineering express
Anatomical segmentations generated using artificial intelligence (AI) have the potential to significantly improve video fluoroscopic swallow study (VFS) analysis. AI segments allow for various metrics to be determined without additional time constrai...

Estimating blurless and noise-free Ir-192 source images from gamma camera images for high-dose-rate brachytherapy using a deep-learning approach.

Biomedical physics & engineering express
. Precise monitoring of the position and dwell time of iridium-192 (Ir-192) during high-dose-rate (HDR) brachytherapy is crucial to avoid serious damage to normal tissues. Source imaging using a compact gamma camera is a potential approach for monito...

Deep Learning Models for Predicting Hearing Thresholds Based on Swept-Tone Stimulus-Frequency Otoacoustic Emissions.

Ear and hearing
OBJECTIVES: This study aims to develop deep learning (DL) models for the quantitative prediction of hearing thresholds based on stimulus-frequency otoacoustic emissions (SFOAEs) evoked by swept tones.

Assessing the Potential of a Deep Learning Tool to Improve Fracture Detection by Radiologists and Emergency Physicians on Extremity Radiographs.

Academic radiology
RATIONALE AND OBJECTIVES: To evaluate the standalone performance of a deep learning (DL) based fracture detection tool on extremity radiographs and assess the performance of radiologists and emergency physicians in identifying fractures of the extrem...