AIMC Topic: Neural Networks, Computer

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Generative adversarial networks in dental imaging: a systematic review.

Oral radiology
OBJECTIVES: This systematic review on generative adversarial network (GAN) architectures for dental image analysis provides a comprehensive overview to readers regarding current GAN trends in dental imagery and potential future applications.

A review on brain tumor segmentation based on deep learning methods with federated learning techniques.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Brain tumors have become a severe medical complication in recent years due to their high fatality rate. Radiologists segment the tumor manually, which is time-consuming, error-prone, and expensive. In recent years, automated segmentation based on dee...

Forecasting water quality variable using deep learning and weighted averaging ensemble models.

Environmental science and pollution research international
Water quality variables, including chlorophyll-a (Chl-a), play a pivotal role in comprehending and evaluating the condition of aquatic ecosystems. Chl-a, a pigment present in diverse aquatic organisms, notably algae and cyanobacteria, serves as a val...

Accuracy of artificial intelligence model for infectious keratitis classification: a systematic review and meta-analysis.

Frontiers in public health
BACKGROUND: Infectious keratitis (IK) is a sight-threatening condition requiring immediate definite treatment. The need for prompt treatment heavily depends on timely diagnosis. The diagnosis of IK, however, is challenged by the drawbacks of the curr...

Investigation of latent representation of toxicopathological images extracted by CNN model for understanding compound properties in vivo.

Computers in biology and medicine
Toxicopathological images acquired during safety assessment elucidate an individual's biological responses to a given compound, and their numerization can yield valuable insights contributing to the assessment of compound properties. Currently, toxic...

LM-Net: A light-weight and multi-scale network for medical image segmentation.

Computers in biology and medicine
Current medical image segmentation approaches have limitations in deeply exploring multi-scale information and effectively combining local detail textures with global contextual semantic information. This results in over-segmentation, under-segmentat...

Salient Arithmetic Data Extraction from Brain Activity via an Improved Deep Network.

Sensors (Basel, Switzerland)
Interpretation of neural activity in response to stimulations received from the surrounding environment is necessary to realize automatic brain decoding. Analyzing the brain recordings corresponding to visual stimulation helps to infer the effects of...

CREaTor: zero-shot cis-regulatory pattern modeling with attention mechanisms.

Genome biology
Linking cis-regulatory sequences to target genes has been a long-standing challenge. In this study, we introduce CREaTor, an attention-based deep neural network designed to model cis-regulatory patterns for genomic elements up to 2 Mb from target gen...

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...

Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry.

Scientific reports
A primary challenge of high-throughput imaging flow cytometry (IFC) is to analyze the vast amount of imaging data, especially in applications where ground truth labels are unavailable or hard to obtain. We present an unsupervised deep embedding algor...