AI Medical Compendium Topic:
Image Interpretation, Computer-Assisted

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Artificial intelligence enhances whole-slide interpretation of PD-L1 CPS in triple-negative breast cancer: A multi-institutional ring study.

Histopathology
BACKGROUND AND AIMS: Evaluation of the programmed cell death ligand-1 (PD-L1) combined positive score (CPS) is vital to predict the efficacy of the immunotherapy in triple-negative breast cancer (TNBC), but pathologists show substantial variability i...

MFMSNet: A Multi-frequency and Multi-scale Interactive CNN-Transformer Hybrid Network for breast ultrasound image segmentation.

Computers in biology and medicine
Breast tumor segmentation in ultrasound images is fundamental for quantitative analysis and plays a crucial role in the diagnosis and treatment of breast cancer. Recently, existing methods have mainly focused on spatial domain implementations, with l...

Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor.

BMC medical imaging
Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. ...

Performance of artificial intelligence chatbots in interpreting clinical images of pressure injuries.

Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society
To evaluate the accuracy of AI chatbots in staging pressure injuries according to the National Pressure Injury Advisory Panel (NPIAP) Staging through clinical image interpretation, a cross-sectional design was conducted to assess five leading publicl...

Robust prostate disease classification using transformers with discrete representations.

International journal of computer assisted radiology and surgery
PURPOSE: Automated prostate disease classification on multi-parametric MRI has recently shown promising results with the use of convolutional neural networks (CNNs). The vision transformer (ViT) is a convolutional free architecture which only exploit...

D-TrAttUnet: Toward hybrid CNN-transformer architecture for generic and subtle segmentation in medical images.

Computers in biology and medicine
Over the past two decades, machine analysis of medical imaging has advanced rapidly, opening up significant potential for several important medical applications. As complicated diseases increase and the number of cases rises, the role of machine-base...

Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50.

BMC medical imaging
This study addresses the critical challenge of detecting brain tumors using MRI images, a pivotal task in medical diagnostics that demands high accuracy and interpretability. While deep learning has shown remarkable success in medical image analysis,...

Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new a...

Voxel level dense prediction of acute stroke territory in DWI using deep learning segmentation models and image enhancement strategies.

Japanese journal of radiology
PURPOSE: To build a stroke territory classifier model in DWI by designing the problem as a multiclass segmentation task by defining each stroke territory as distinct segmentation targets and leveraging the guidance of voxel wise dense predictions.

Assessing deep learning reconstruction for faster prostate MRI: visual vs. diagnostic performance metrics.

European radiology
OBJECTIVE: Deep learning (DL) MRI reconstruction enables fast scan acquisition with good visual quality, but the diagnostic impact is often not assessed because of large reader study requirements. This study used existing diagnostic DL to assess the ...