AIMC Topic: Image Processing, Computer-Assisted

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Multiclass ensemble framework for enhanced prostate gland Segmentation: Integrating Self-ONN decoders with EfficientNet.

Computers in biology and medicine
Digital pathology relies on the morphological architecture of prostate glands to recognize cancerous tissue. Prostate cancer (PCa) originates in walnut shaped prostate gland in the male reproductive system. Deep learning (DL) pipelines can assist in ...

A combined attention mechanism for brain tumor segmentation of lower-grade glioma in magnetic resonance images.

Computers in biology and medicine
Low-grade gliomas (LGGs) are among the most problematic brain tumors to reliably segment in FLAIR MRI, and effective delineation of these lesions is critical for clinical diagnosis, treatment planning, and patient monitoring. Nevertheless, convention...

MHS U-Net: Multi-scale hybrid subtraction network for medical image segmentation.

Computers in biology and medicine
Medical image segmentation plays a critical role in modern clinical diagnosis. However, existing methods face challenges such as insufficient feature extraction, limited spatial modeling capabilities, and restricted generalization ability with low co...

Deep learning-based histopathologic segmentation of peritubular capillaries in kidney transplant biopsies.

Computers in biology and medicine
BACKGROUND: Assessing the extent of inflammation in peritubular capillaries (PTCs) is important for diagnosing antibody-mediated rejection in kidney transplant biopsies. However, this assessment is time-consuming and suffers from interobserver variab...

Weakly-supervised semantic segmentation in histology images using contrastive learning and self-training.

Computers in biology and medicine
This paper presents a novel method for weakly-supervised semantic segmentation (WSSS) of histology images, where only global image-level labels are employed. We leverage an existing weakly-supervised object localization (WSOL) method to generate clas...

A general survey on medical image super-resolution via deep learning.

Computers in biology and medicine
Medical image super-resolution (SR) is a classic regression task in low-level vision. Limited by hardware limitations, acquisition time, low radiation dose, and other factors, the spatial resolution of some medical images is not sufficient. To addres...

Prostate cancer prediction through a hybrid deep learning method applied to histopathological image.

Expert review of anticancer therapy
BACKGROUND: Prostate Cancer (PCa) is a severe disease that affects males globally. The Gleason grading system is a widely recognized method for diagnosing the aggressiveness of PCa using histopathological images. This system evaluates prostate tissue...

CancerNet: A comprehensive deep learning framework for precise and intelligible cancer identification.

Computers in biology and medicine
The medical community continually seeks innovative solutions to address healthcare challenges, particularly in cancer detection. A promising approach involves the use of Artificial Intelligence (AI) techniques, specifically Deep Learning (DL) models....

Transformation trees - Documentation of multimodal image registration.

Computers in biology and medicine
Multimodal image registration plays a key role in creating digital patient models by combining data from different imaging techniques into a single coordinate system. This process often involves multiple sequential and interconnected transformations,...

Trends and advances in image-based mosquito identification and classification using machine learning models: A systematic review.

Computers in biology and medicine
Mosquito-borne diseases, such as Yellow fever, Dengue, and Zika, pose a significant global health threat, causing millions of deaths annually. Traditional mosquito identification methods, reliant on expert analysis, are time-consuming and resource-in...