Application of artificial intelligence in pathology: trends and challenges
I Kim, K Kang, Y Song, TJ Kim - Diagnostics, 2022 - mdpi.com
Given the recent success of artificial intelligence (AI) in computer vision applications, many
pathologists anticipate that AI will be able to assist them in a variety of digital pathology …
pathologists anticipate that AI will be able to assist them in a variety of digital pathology …
Multi-modality artificial intelligence in digital pathology
In common medical procedures, the time-consuming and expensive nature of obtaining test
results plagues doctors and patients. Digital pathology research allows using computational …
results plagues doctors and patients. Digital pathology research allows using computational …
[HTML][HTML] Cellvit: Vision transformers for precise cell segmentation and classification
Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images
are important clinical tasks and crucial for a wide range of applications. However, it is a …
are important clinical tasks and crucial for a wide range of applications. However, it is a …
[HTML][HTML] One model is all you need: multi-task learning enables simultaneous histology image segmentation and classification
The recent surge in performance for image analysis of digitised pathology slides can largely
be attributed to the advances in deep learning. Deep models can be used to initially localise …
be attributed to the advances in deep learning. Deep models can be used to initially localise …
Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions
Over the past few years, the rapid development of deep learning technologies for computer
vision has significantly improved the performance of medical image segmentation …
vision has significantly improved the performance of medical image segmentation …
Multi-scale hypergraph-based feature alignment network for cell localization
Cell localization in medical image analysis is a challenging task due to the significant
variation in cell shape, size and color. Existing localization methods continue to tackle these …
variation in cell shape, size and color. Existing localization methods continue to tackle these …
Exponential distance transform maps for cell localization
Cell localization in medical image analysis aims for precise identification of cell positions.
Existing methods involve predicting density maps from images, followed by post-processing …
Existing methods involve predicting density maps from images, followed by post-processing …
Restaining-based annotation for cancer histology segmentation to overcome annotation-related limitations among pathologists
Numerous cancer histopathology specimens have been collected and digitized over the
past few decades. A comprehensive evaluation of the distribution of various cells in tumor …
past few decades. A comprehensive evaluation of the distribution of various cells in tumor …
Nuclei and glands instance segmentation in histology images: a narrative review
Examination of tissue biopsy and quantification of the various characteristics of cellular
processes are clinical benchmarks in cancer diagnosis. Nuclei and glands instance …
processes are clinical benchmarks in cancer diagnosis. Nuclei and glands instance …
[HTML][HTML] Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer
N Zamanitajeddin, M Jahanifar, M Bilal… - Medical Image …, 2024 - Elsevier
Colorectal cancer (CRC) is a primary global health concern, and identifying the molecular
pathways, genetic subtypes, and mutations associated with CRC is crucial for precision …
pathways, genetic subtypes, and mutations associated with CRC is crucial for precision …