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Spleen

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Deep Learning-based U-Mamba Model to Predict Differentiated Gastric Cancer using Radiomics Features from Spleen Segmentation.

Current medical imaging
OBJECTIVE: This study aimed to develop an automated method for segmenting spleen computed tomography (CT) images using a deep learning model to address the limitations of manual segmentation, which is known to be susceptible to inter-observer variabi...

Exposure Pathways of Ambient Magnetite Nanoparticles Revealed by Machine Learning-Aided Single-Particle Mass Spectrometry.

Nano letters
Nanosized ultrafine particles (UFPs) from natural and anthropogenic sources are widespread and pose serious health risks when inhaled by humans. However, tracing the inhaled UFPs is extremely difficult, and the distribution, translocation, and metab...

Heatstroke death identification using ATR-FTIR spectroscopy combined with a novel multi-organ machine learning approach.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
With global warming, the number of deaths due to heatstroke has drastically increased. Nevertheless, there are still difficulties with the forensic assessment of heatstroke deaths, including the absence of particular organ pathological abnormalities ...

Deep learning reconstruction for accelerated high-resolution upper abdominal MRI improves lesion detection without time penalty.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to compare a conventional T1-weighted volumetric interpolated breath-hold examination (VIBE) sequence with a DL-reconstructed accelerated high-resolution VIBE sequence (HR-VIBE) in terms of image quality, lesion...

Immunohistochemistry-Free Enhanced Histopathology of the Rat Spleen Using Deep Learning.

Toxicologic pathology
Enhanced histopathology of the immune system uses a precise, compartment-specific, and semi-quantitative evaluation of lymphoid organs in toxicology studies. The assessment of lymphocyte populations in tissues is subject to sampling variability and l...

The impact of the novel CovBat harmonization method on enhancing radiomics feature stability and machine learning model performance: A multi-center, multi-device study.

European journal of radiology
PURPOSE: This study aims to assess whether the novel CovBat harmonization method can further reduce radiomics feature variability from different imaging devices in multi-center studies and improve machine learning model performance compared to the Co...

Improving spleen segmentation in ultrasound images using a hybrid deep learning framework.

Scientific reports
This paper introduces a novel method for spleen segmentation in ultrasound images, using a two-phase training approach. In the first phase, the SegFormerB0 network is trained to provide an initial segmentation. In the second phase, the network is fur...

RSNA 2023 Abdominal Trauma AI Challenge: Review and Outcomes.

Radiology. Artificial intelligence
Purpose To evaluate the performance of the winning machine learning models from the 2023 RSNA Abdominal Trauma Detection AI Challenge. Materials and Methods The competition was hosted on Kaggle and took place between July 26 and October 15, 2023. The...

A novel approach to the cause of death identification-multi-strategy integration of multi-organ FTIR spectroscopy information using machine learning.

Talanta
Identifying the cause of death has always been a major focus and challenge in forensic practice and research. Traditional techniques for determining the causes of death are time-consuming, labor-intensive, have high professional barriers, and are vul...

Deep learning and radiomics for gastric cancer serosal invasion: automated segmentation and multi-machine learning from two centers.

Journal of cancer research and clinical oncology
OBJECTIVE: The objective of this study is to develop an automated method for segmenting spleen computed tomography (CT) images using a deep learning model. This approach is intended to address the limitations of manual segmentation, which is known to...