AIMC Topic: Deep Learning

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Deep reinforcement learning control as an innovative approach for urban drainage systems: review and prospects.

Water research
Urban drainage systems (UDSs) are vital for managing stormwater and wastewater but face growing challenges due to urbanization, climate change and aging infrastructure. Real-time control (RTC) enhances UDSs' performance and circumvents the need for s...

Predicting clinical prognosis in gastric cancer using deep learning-based analysis of tissue pathomics images.

Computer methods and programs in biomedicine
OBJECTIVE: Evaluate the utility of a machine learning-based pathomics model in predicting overall survival (OS) post-surgery for gastric cancer patients.

Automated phenotypic analysis and classification of drug-treated cardiomyocytes via synergized time-lapse holographic imaging and deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Predicting cardiovascular risk is critical for the therapy and control of cardiovascular illnesses. This work studies screening the toxicity of three drugs, (E-4031, isoprenaline, and sertindole) with various concentrations ...

Machine learning-based histopathological features of histological slides and clinical characteristics as a novel prognostic indicator in diffuse large B-cell lymphoma.

Pathology, research and practice
OBJECTIVE: This study developed and validated a deep learning model based on clinical and histopathological features for predicting the outcomes of diffuse large B-cell lymphoma (DLBCL).

UltraBones100k: A reliable automated labeling method and large-scale dataset for ultrasound-based bone surface extraction.

Computers in biology and medicine
BACKGROUND: Ultrasound-based bone surface segmentation is crucial in computer-assisted orthopedic surgery. However, ultrasound images have limitations, including a low signal-to-noise ratio, acoustic shadowing, and speckle noise, which make interpret...

Vascular segmentation of functional ultrasound images using deep learning.

Computers in biology and medicine
Segmentation of medical images is a fundamental task with numerous applications. While MRI, CT, and PET modalities have significantly benefited from deep learning segmentation techniques, more recent modalities, like functional ultrasound (fUS), have...

Preoperative Identification of Papillary Thyroid Carcinoma Subtypes and Lymph Node Metastasis via Deep Learning-Assisted Surface-Enhanced Raman Spectroscopy.

ACS nano
Accurate preoperative diagnosis of papillary thyroid carcinoma (PTC) histological subtypes and lymph node metastasis is essential for formulating personalized treatment strategies. However, their preoperative diagnosis is challenged by the limited re...

Amortized template matching of molecular conformations from cryoelectron microscopy images using simulation-based inference.

Proceedings of the National Academy of Sciences of the United States of America
Characterizing the conformational ensemble of biomolecular systems is key to understand their functions. Cryoelectron microscopy (cryo-EM) captures two-dimensional snapshots of biomolecular ensembles, giving in principle access to thermodynamics. How...

Advancing blood cell detection and classification: performance evaluation of modern deep learning models.

BMC medical informatics and decision making
The detection and classification of blood cells are important in diagnosing and monitoring a variety of blood-related illnesses, such as anemia, leukemia, and infection, all of which may cause significant mortality. Accurate blood cell identification...

Deep learning model applied to real-time delineation of colorectal polyps.

BMC medical informatics and decision making
BACKGROUND: Deep learning models have shown considerable potential to improve diagnostic accuracy across medical fields. Although YOLACT has demonstrated real-time detection and segmentation in non-medical datasets, its application in medical setting...