Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy
C McGenity, EL Clarke, C Jennings, G Matthews… - npj Digital …, 2024 - nature.com
Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical
practice is essential. Growing numbers of studies using AI for digital pathology have been …
practice is essential. Growing numbers of studies using AI for digital pathology have been …
A comprehensive survey of intestine histopathological image analysis using machine vision approaches
Y **g, C Li, T Du, T Jiang, H Sun, J Yang, L Shi… - Computers in Biology …, 2023 - Elsevier
Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is
the third most common malignancy and the fourth leading cause of cancer death worldwide …
the third most common malignancy and the fourth leading cause of cancer death worldwide …
CS-CO: A hybrid self-supervised visual representation learning method for H&E-stained histopathological images
Visual representation extraction is a fundamental problem in the field of computational
histopathology. Considering the powerful representation capacity of deep learning and the …
histopathology. Considering the powerful representation capacity of deep learning and the …
Elastic deep multi-view autoencoder with diversity embedding
Current research on multi-view clustering (MVC) is pushing the boundaries of knowledge,
allowing the extraction of valuable insights from various points of view. Recently, many …
allowing the extraction of valuable insights from various points of view. Recently, many …
Multi-level multi-type self-generated knowledge fusion for cardiac ultrasound segmentation
Most existing works on cardiac echocardiography segmentation require a large number of
ground-truth labels to appropriately train a neural network; this, however, is time consuming …
ground-truth labels to appropriately train a neural network; this, however, is time consuming …
Prediction consistency regularization for generalized category discovery
Abstract Generalized Category Discovery (GCD) is a recently proposed open-world problem
that aims to automatically discover and cluster based on partially labeled data. The …
that aims to automatically discover and cluster based on partially labeled data. The …
Automated detection of premalignant oral lesions on whole slide images using convolutional neural networks
Introduction Oral epithelial dysplasia (OED) is a precursor lesion to oral squamous cell
carcinoma, a disease with a reported overall survival rate of 56 percent across all stages …
carcinoma, a disease with a reported overall survival rate of 56 percent across all stages …
Dynamic prototypical feature representation learning framework for semi-supervised skin lesion segmentation
Automated skin lesion segmentation is an essential yet challenging task for computer-aided
skin disease diagnosis. One major challenge for learning-based segmentation method is the …
skin disease diagnosis. One major challenge for learning-based segmentation method is the …
Artificial intelligence applications in histopathology
Histopathology is a vital diagnostic discipline in medicine, fundamental to our
understanding, detection, assessment and treatment of conditions such as cancer, dementia …
understanding, detection, assessment and treatment of conditions such as cancer, dementia …
Application of histopathology image analysis using deep learning networks
As the rise in cancer cases, there is an increasing demand to develop accurate and rapid
diagnostic tools for early intervention. Pathologists are looking to augment manual analysis …
diagnostic tools for early intervention. Pathologists are looking to augment manual analysis …