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 …
SMILE: Cost-sensitive multi-task learning for nuclear segmentation and classification with imbalanced annotations
High throughput nuclear segmentation and classification of whole slide images (WSIs) is
crucial to biological analysis, clinical diagnosis and precision medicine. With the advances …
crucial to biological analysis, clinical diagnosis and precision medicine. With the advances …
Transformer-based meta learning method for bearing fault identification under multiple small sample conditions
X Li, H Su, L Xiang, Q Yao, A Hu - Mechanical Systems and Signal …, 2024 - Elsevier
Most fault identification methods based on deep learning rely on a large amount of data, and
their effects are limited in the actual production environment. In the case of multiple …
their effects are limited in the actual production environment. In the case of multiple …
WBC YOLO-ViT: 2 Way-2 stage white blood cell detection and classification with a combination of YOLOv5 and vision transformer
Accurate detection and classification of white blood cells, otherwise known as leukocytes,
play a critical role in diagnosing and monitoring various illnesses. However, conventional …
play a critical role in diagnosing and monitoring various illnesses. However, conventional …
Image analysis of nuclei histopathology using deep learning: A review of segmentation, detection, and classification
M Kadaskar, N Patil - SN Computer Science, 2023 - Springer
Deep learning has recently advanced in its applicability to computer vision challenges, and
medical imaging has become the most used technique in histopathology image analysis …
medical imaging has become the most used technique in histopathology image analysis …
[HTML][HTML] Learning what and where to segment: A new perspective on medical image few-shot segmentation
Y Feng, Y Wang, H Li, M Qu, J Yang - Medical Image Analysis, 2023 - Elsevier
Traditional medical image segmentation methods based on deep learning require experts to
provide extensive manual delineations for model training. Few-shot learning aims to reduce …
provide extensive manual delineations for model training. Few-shot learning aims to reduce …
Maize plant detection using UAV-based RGB imaging and YOLOv5
In recent years, computer vision (CV) has made enormous progress and is providing great
possibilities in analyzing images for object detection, especially with the application of …
possibilities in analyzing images for object detection, especially with the application of …
Domain generalization in computational pathology: survey and guidelines
Deep learning models have exhibited exceptional effectiveness in Computational Pathology
(CPath) by tackling intricate tasks across an array of histology image analysis applications …
(CPath) by tackling intricate tasks across an array of histology image analysis applications …
Semi-supervised nuclei segmentation based on multi-edge features fusion attention network
H Li, J Zhong, L Lin, Y Chen, P Shi - Plos one, 2023 - journals.plos.org
The morphology of the nuclei represents most of the clinical pathological information, and
nuclei segmentation is a vital step in current automated histopathological image analysis …
nuclei segmentation is a vital step in current automated histopathological image analysis …
Seine: Structure encoding and interaction network for nuclei instance segmentation
Y Zhang, L Cai, Z Wang, Y Zhang - arXiv preprint arXiv:2401.09773, 2024 - arxiv.org
Nuclei instance segmentation in histopathological images is of great importance for
biological analysis and cancer diagnosis but remains challenging for two reasons.(1) Similar …
biological analysis and cancer diagnosis but remains challenging for two reasons.(1) Similar …