AIMC Topic: Deep Learning

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Deep learning model for differentiating nasal cavity masses based on nasal endoscopy images.

BMC medical informatics and decision making
BACKGROUND: Nasal polyps and inverted papillomas often look similar. Clinically, it is difficult to distinguish the masses by endoscopic examination. Therefore, in this study, we aimed to develop a deep learning algorithm for computer-aided diagnosis...

Dev-ResNet: automated developmental event detection using deep learning.

The Journal of experimental biology
Delineating developmental events is central to experimental research using early life stages, permitting widespread identification of changes in event timing between species and environments. Yet, identifying developmental events is incredibly challe...

Panoramic Radiography in the Evaluation of the Relationship of Maxillary Molar Teeth and Maxillary Sinuses on the Deep Learning Models Improved with the Findings Obtained by Cone Beam Computed Tomography.

Nigerian journal of clinical practice
BACKGROUND: Panoramic radiography (PR) is available to determine the contact relationship between maxillary molar teeth (MMT) and the maxillary sinus floor (MSF). However, as PRs do not provide clear and detailed anatomical information, advanced imag...

Leveraging conformal prediction to annotate enzyme function space with limited false positives.

PLoS computational biology
Machine learning (ML) is increasingly being used to guide biological discovery in biomedicine such as prioritizing promising small molecules in drug discovery. In those applications, ML models are used to predict the properties of biological systems,...

Forecasting fish mortality from water and air quality data using deep learning models.

Journal of environmental quality
The high rate of aquatic mortality incidents recorded in Taiwan and worldwide is creating an urgent demand for more accurate fish mortality prediction. Present study innovatively integrated air and water quality data to measure water quality degradat...

Effective prediction of human skin cancer using stacking based ensemble deep learning algorithm.

Network (Bristol, England)
Automated diagnosis of cancer from skin lesion data has been the focus of numerous research. Despite that it can be challenging to interpret these images because of features like colour illumination changes, variation in the sizes and forms of the le...

OrganoIDNet: a deep learning tool for identification of therapeutic effects in PDAC organoid-PBMC co-cultures from time-resolved imaging data.

Cellular oncology (Dordrecht, Netherlands)
PURPOSE: Pancreatic Ductal Adenocarcinoma (PDAC) remains a challenging disease due to its complex biology and aggressive behavior with an urgent need for efficient therapeutic strategies. To assess therapy response, pre-clinical PDAC organoid-based m...

Classification of Caries Based on CBCT: A Deep Learning Network Interpretability Study.

Journal of imaging informatics in medicine
This study aimed to create a caries classification scheme based on cone-beam computed tomography (CBCT) and develop two deep learning models to improve caries classification accuracy. A total of 2713 axial slices were obtained from CBCT images of 204...

MRI-based Deep Learning Models for Preoperative Breast Volume and Density Assessment Assisting Breast Reconstruction.

Aesthetic plastic surgery
BACKGROUND: The volume of the implant is the most critical element of breast reconstruction, so it is necessary to accurately assess the preoperative volume of the healthy and affected breasts and select the appropriate implant for placement. Accurat...

An effective deep learning fusion method for predicting the TVB-N and TVC contents of chicken breasts using dual hyperspectral imaging systems.

Food chemistry
Total volatile basic nitrogen (TVB-N) and total viable count (TVC) are important freshness indicators of meat. Hyperspectral imaging combined with chemometrics has been proven to be effective in meat detection. However, a challenge with chemometrics ...