Deep learning

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News • Imaging equipment

Fujifilm presents new MRI scanner at ECR 2024

Fujifilm Healthcare Europe will present its Echelon Synergy MRI system at the European Congress of Radiology 2024. The 1.5 T scanner employs AI features to enhance image quality and scanning speed.

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News • Imaging signatures

CT-based radiomics deep learning to predict lymph node metastasis in tumors

With a combination of radiomics and deep learning, researchers aim to noninvasively determine lymph node metastasis before surgery. This could lead to more accurate diagnosis and treatment strategies.

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News • Finding therapy-relevant genetics

Leukaemia: AI provides support in diagnostics

Certain genetic features are crucial for treatment decisions for AML leukaemia. A team from Münster shows how an AI-based method can predict these features from images of bone marrow smears.

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News • WSI-based analysis

AI-driven classification of diffuse gliomas skips molecular testing

Research from Shenzhen proposes an integrated diagnosis model for automatic classification of adult-type diffuse gliomas directly from annotation-free standard whole-slide pathological images.

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News • Susceptibility detection in Escherichia coli

How AI can detect antibiotic resistance in 30 minutes

A new deep-learning approach to AMR testing has been shown to detect antimicrobial susceptibility within as little as 30 minutes - significantly faster than current gold-standard approaches.

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Article • Need for diversity in training datasets

Artificial intelligence in healthcare: not always fair

Machine learning and artificial intelligence (AI) are playing an increasingly important role in medicine and healthcare, and not just since ChatGPT. This is especially true in data-intensive…

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Supervised learning approach

A new deep learning-based algorithm to predict relapse-free survival in papillary thyroid carcinoma

The tall cell variant (TCV) is an aggressive subtype of papillary thyroid carcinoma (PTC). Sebastian Stenman, researcher from the Institute for Molecular Medicine, and the Department of Pathology at the University of Helsinki, Finland, is developing and training a deep learning algorithm using supervised learning to detect and quantify the proportion of tall cells in PTC.

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