AIMC Topic: Image Processing, Computer-Assisted

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Automatic data augmentation to improve generalization of deep learning in H&E stained histopathology.

Computers in biology and medicine
In histopathology practice, scanners, tissue processing, staining, and image acquisition protocols vary from center to center, resulting in subtle variations in images. Vanilla convolutional neural networks are sensitive to such domain shifts. Data a...

Evidence-based uncertainty-aware semi-supervised medical image segmentation.

Computers in biology and medicine
Semi-Supervised Learning (SSL) has demonstrated great potential to reduce the dependence on a large set of annotated data, which is challenging to collect in clinical practice. One of the most important SSL methods is to generate pseudo labels from t...

A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction.

Sensors (Basel, Switzerland)
Deep learning (DL) in magnetic resonance imaging (MRI) shows excellent performance in image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI reconstruction is essential for ensuring accurate diagnosis, supporting clin...

Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer.

BMC medical imaging
With the rapid development of medical imaging technology and computer technology, the medical imaging artificial intelligence of computer-aided diagnosis based on machine learning has become an important part of modern medical diagnosis. With the app...

Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation.

Physics in medicine and biology
. Prior to radiation therapy planning, accurate delineation of gross tumour volume (GTVs) and organs at risk (OARs) is crucial. In the current clinical practice, tumour delineation is performed manually by radiation oncologists, which is time-consumi...

Gross failure rates and failure modes for a commercial AI-based auto-segmentation algorithm in head and neck cancer patients.

Journal of applied clinical medical physics
PURPOSE: Artificial intelligence (AI) based commercial software can be used to automatically delineate organs at risk (OAR), with potential for efficiency savings in the radiotherapy treatment planning pathway, and reduction of inter- and intra-obser...

Applications of artificial intelligence in the field of oral and maxillofacial pathology: a systematic review and meta-analysis.

BMC oral health
BACKGROUND: Since AI algorithms can analyze patient data, medical records, and imaging results to suggest treatment plans and predict outcomes, they have the potential to support pathologists and clinicians in the diagnosis and treatment of oral and ...

Evaluation of a deep image-to-image network (DI2IN) auto-segmentation algorithm across a network of cancer centers.

Journal of cancer research and therapeutics
PURPOSE/OBJECTIVE S: Due to manual OAR contouring challenges, various automatic contouring solutions have been introduced. Historically, common clinical auto-segmentation algorithms used were atlas-based, which required maintaining a library of self-...

AI-Enhanced Detection of Clinically Relevant Structural and Functional Anomalies in MRI: Traversing the Landscape of Conventional to Explainable Approaches.

Journal of magnetic resonance imaging : JMRI
Anomaly detection in medical imaging, particularly within the realm of magnetic resonance imaging (MRI), stands as a vital area of research with far-reaching implications across various medical fields. This review meticulously examines the integratio...