AIMC Topic: Image Processing, Computer-Assisted

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Semi-Supervised Learning Allows for Improved Segmentation With Reduced Annotations of Brain Metastases Using Multicenter MRI Data.

Journal of magnetic resonance imaging : JMRI
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 ...

BMA-Net: A 3D bidirectional multi-scale feature aggregation network for prostate region segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Accurate segmentation of the prostate region in magnetic resonance imaging (MRI) is crucial for prostate-related diagnoses. Recent studies have incorporated Transformers into prostate region segmentation to better capture lo...

Colorectal cancer classification using weakly annotated whole slide images: Multiple instance learning optimization study.

Computers in biology and medicine
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
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...

DP-CLAM: A weakly supervised benign-malignant classification study based on dual-angle scanning ultrasound images of thyroid nodules.

Medical engineering & physics
In this paper, a two-stage task weakly supervised learning algorithm is proposed. It accurately achieved patient-level classification task of benign and malignant thyroid nodules based on ultrasound images from two scanning angles: long axis and shor...

A high-throughput ResNet CNN approach for automated grapevine leaf hair quantification.

Scientific reports
The hairiness of the leaves is an essential morphological feature within the genus Vitis that can serve as a physical barrier. A high leaf hair density present on the abaxial surface of the grapevine leaves influences their wettability by repelling f...

Robustly detecting mpox and non-mpox using a deep learning framework based on image inpainting.

Scientific reports
Due to the lack of efficient mpox diagnostic technology, mpox cases continue to increase. Recently, the great potential of deep learning models in detecting mpox and non-mpox has been proven. However, existing methods are susceptible to interference ...

A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images.

Scientific reports
Brain tumors present a significant global health challenge, and their early detection and accurate classification are crucial for effective treatment strategies. This study presents a novel approach combining a lightweight parallel depthwise separabl...

Hypothalamic atrophy in primary lateral sclerosis, assessed by convolutional neural network-based automatic segmentation.

Scientific reports
Primary lateral sclerosis (PLS) is a motor neuron disease (MND) which mainly affects upper motor neurons. Within the MND spectrum, PLS is much more slowly progressive than amyotrophic laterals sclerosis (ALS). `Classical` ALS is characterized by cata...