AIMC Topic: Diagnostic Imaging

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A systematic review on the use of artificial intelligence in gynecologic imaging - Background, state of the art, and future directions.

Gynecologic oncology
OBJECTIVE: Machine learning, deep learning, and artificial intelligence (AI) are terms that have made their way into nearly all areas of medicine. In the case of medical imaging, these methods have become the state of the art in nearly all areas from...

Combined Mueller matrix imaging and artificial intelligence classification framework for Hepatitis B detection.

Journal of biomedical optics
SIGNIFICANCE: The combination of polarized imaging with artificial intelligence (AI) technology has provided a powerful tool for performing an objective and precise diagnosis in medicine.

Interpretability-Guided Inductive Bias For Deep Learning Based Medical Image.

Medical image analysis
Deep learning methods provide state of the art performance for supervised learning based medical image analysis. However it is essential that trained models extract clinically relevant features for downstream tasks as, otherwise, shortcut learning an...

Bridging 3D Slicer and ROS2 for Image-Guided Robotic Interventions.

Sensors (Basel, Switzerland)
Developing image-guided robotic systems requires access to flexible, open-source software. For image guidance, the open-source medical imaging platform 3D Slicer is one of the most adopted tools that can be used for research and prototyping. Similarl...

DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches.

Communications biology
This work demonstrates and guides how to use a range of state-of-the-art artificial neural-networks to analyse bacterial microscopy images using the recently developed ZeroCostDL4Mic platform. We generated a database of image datasets used to train n...

[Artificial intelligence and hyperspectral imaging for image-guided assistance in minimally invasive surgery].

Chirurgie (Heidelberg, Germany)
BACKGROUND: Intraoperative imaging assists surgeons during minimally invasive procedures. Hyperspectral imaging (HSI) is a noninvasive and noncontact optical technique with great diagnostic potential in medicine. The combination with artificial intel...

Double-branch U-Net for multi-scale organ segmentation.

Methods (San Diego, Calif.)
U-Net has achieved great success in the task of medical image segmentation. It encodes and extracts information from several convolution blocks, and then decodes the feature maps to get the segmentation results. Our experiments show that in a multi-s...

Deep learning in veterinary medicine, an approach based on CNN to detect pulmonary abnormalities from lateral thoracic radiographs in cats.

Scientific reports
Thoracic radiograph (TR) is a complementary exam widely used in small animal medicine which requires a sharp analysis to take full advantage of Radiographic Pulmonary Pattern (RPP). Although promising advances have been made in deep learning for vete...

A Modified Deep Learning Framework for Arrhythmia Disease Analysis in Medical Imaging Using Electrocardiogram Signal.

BioMed research international
Arrhythmias are anomalies in the heartbeat rhythm that occur occasionally in people's lives. These arrhythmias can lead to potentially deadly consequences, putting your life in jeopardy. As a result, arrhythmia identification and classification are a...