AIMC Topic: Deep Learning

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Assessment of the uncertainty and interpretability of deep learning models for mapping soil salinity using DeepQuantreg and game theory.

Scientific reports
This research introduces a new combined modelling approach for mapping soil salinity in the Minab plain in southern Iran. This study assessed the uncertainty (with 95% confidence limits) and interpretability of two deep learning (DL) models (deep bol...

Deep learning multi-organ segmentation for whole mouse cryo-images including a comparison of 2D and 3D deep networks.

Scientific reports
Cryo-imaging provided 3D whole-mouse microscopic color anatomy and fluorescence images that enables biotechnology applications (e.g., stem cells and metastatic cancer). In this report, we compared three methods of organ segmentation: 2D U-Net with 2D...

Deep learning based optimal energy management for photovoltaic and battery energy storage integrated home micro-grid system.

Scientific reports
The development of the advanced metering infrastructure (AMI) and the application of artificial intelligence (AI) enable electrical systems to actively engage in smart grid systems. Smart homes with energy storage systems (ESS) and renewable energy s...

Hybrid of deep learning and exponential smoothing for enhancing crime forecasting accuracy.

PloS one
The continued urbanization poses several challenges for law enforcement agencies to ensure a safe and secure environment. Countries are spending a substantial amount of their budgets to control and prevent crime. However, limited efforts have been ma...

Diagnostic Assessment of Deep Learning Algorithms for Frozen Tissue Section Analysis in Women with Breast Cancer.

Cancer research and treatment
PURPOSE: Assessing the metastasis status of the sentinel lymph nodes (SLNs) for hematoxylin and eosin-stained frozen tissue sections by pathologists is an essential but tedious and time-consuming task that contributes to accurate breast cancer stagin...

A novel multimodal deep learning model for preoperative prediction of microvascular invasion and outcome in hepatocellular carcinoma.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
BACKGROUND: Accurate preoperative identification of the microvascular invasion (MVI) can relieve the pressure from personalized treatment adaptation and improve the poor prognosis for hepatocellular carcinoma (HCC). This study aimed to develop and va...

Deep Learning Detection of Active Pulmonary Tuberculosis at Chest Radiography Matched the Clinical Performance of Radiologists.

Radiology
Background The World Health Organization (WHO) recommends chest radiography to facilitate tuberculosis (TB) screening. However, chest radiograph interpretation expertise remains limited in many regions. Purpose To develop a deep learning system (DLS)...

Photon-counting Detector CT with Deep Learning Noise Reduction to Detect Multiple Myeloma.

Radiology
Background Photon-counting detector (PCD) CT and deep learning noise reduction may improve spatial resolution at lower radiation doses compared with energy-integrating detector (EID) CT. Purpose To demonstrate the diagnostic impact of improved spatia...

Machine learning and deep learning modeling and simulation for predicting PM2.5 concentrations.

Chemosphere
Particulate matter (PM) pollution greatly endanger human physical and mental health, and it is of great practical significance to predict PM concentrations accurately. This study measured one-year monitoring data of six main meteorological parameters...