AIMC Topic: Image Interpretation, Computer-Assisted
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Dynamic spectrum-driven hierarchical learning network for polyp segmentation.
Medical image analysis
Jan 14, 2025
Accurate automatic polyp segmentation in colonoscopy is crucial for the prompt prevention of colorectal cancer. However, the heterogeneous nature of polyps and differences in lighting and visibility conditions present significant challenges in achiev...
When multiple instance learning meets foundation models: Advancing histological whole slide image analysis.
Medical image analysis
Jan 14, 2025
Deep multiple instance learning (MIL) pipelines are the mainstream weakly supervised learning methodologies for whole slide image (WSI) classification. However, it remains unclear how these widely used approaches compare to each other, given the rece...
Assessment of different U-Net backbones in segmenting colorectal adenocarcinoma from H&E histopathology.
Pathology, research and practice
Jan 14, 2025
Adenocarcinoma, the most prevalent type of colorectal cancer, makes up roughly 95 % of all cases and is associated with a notably high mortality rate. Owing to the various risk factors which might include personal choices and habits or genetic factor...
Optimizing hip MRI: enhancing image quality and elevating inter-observer consistency using deep learning-powered reconstruction.
BMC medical imaging
Jan 13, 2025
BACKGROUND: Conventional hip joint MRI scans necessitate lengthy scan durations, posing challenges for patient comfort and clinical efficiency. Previously, accelerated imaging techniques were constrained by a trade-off between noise and resolution. L...
Importance of neural network complexity for the automatic segmentation of individual thigh muscles in MRI images from patients with neuromuscular diseases.
Magma (New York, N.Y.)
Jan 11, 2025
OBJECTIVE: Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness...
Magnetic Resonance Imaging
Muscle, Skeletal
Adipose Tissue
Thigh
Image Interpretation, Computer-Assisted
Neuromuscular Diseases
Middle Aged
Deep Learning
Image Processing, Computer-Assisted
Adult
Male
Neural Networks, Computer
Humans
Algorithms
Female
Databases, Factual
Reproducibility of Results
Aged
Not seeing the trees for the forest. The impact of neighbours on graph-based configurations in histopathology.
BMC bioinformatics
Jan 11, 2025
BACKGROUND: Deep learning (DL) has set new standards in cancer diagnosis, significantly enhancing the accuracy of automated classification of whole slide images (WSIs) derived from biopsied tissue samples. To enable DL models to process these large i...
Visualizing Preosteoarthritis: Updates on UTE-Based Compositional MRI and Deep Learning Algorithms.
Journal of magnetic resonance imaging : JMRI
Jan 10, 2025
Osteoarthritis (OA) is heterogeneous and involves structural changes in the whole joint, such as cartilage, meniscus/labrum, ligaments, and tendons, mainly with short T2 relaxation times. Detecting OA before the onset of irreversible changes is cruci...
Semi-Supervised Learning Allows for Improved Segmentation With Reduced Annotations of Brain Metastases Using Multicenter MRI Data.
Journal of magnetic resonance imaging : JMRI
Jan 10, 2025
BACKGROUND: Deep learning-based segmentation of brain metastases relies on large amounts of fully annotated data by domain experts. Semi-supervised learning offers potential efficient methods to improve model performance without excessive annotation ...
Colorectal cancer classification using weakly annotated whole slide images: Multiple instance learning optimization study.
Computers in biology and medicine
Jan 10, 2025
Colorectal cancer (CRC) is considered one of the most deadly cancer types nowadays. It is rapidly increasing due to many factors, such as unhealthy lifestyles, water and food pollution, aging, and medical diagnosis development. Detecting CRC in its e...
FocusUNet: Pioneering dual attention with gated U-Net for colonoscopic polyp segmentation.
Computers in biology and medicine
Jan 10, 2025
The detection and excision of colorectal polyps, precursors to colorectal cancer (CRC), can improve survival rates by up to 90%. Automated polyp segmentation in colonoscopy images expedites diagnosis and aids in the precise identification of adenomat...
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