A survey on applications of deep learning in microscopy image analysis
Z Liu, L Jin, J Chen, Q Fang, S Ablameyko, Z Yin… - Computers in biology …, 2021 - Elsevier
Advanced microscopy enables us to acquire quantities of time-lapse images to visualize the
dynamic characteristics of tissues, cells or molecules. Microscopy images typically vary in …
dynamic characteristics of tissues, cells or molecules. Microscopy images typically vary in …
Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: a systematic review
This systematic review analyses and describes the application and diagnostic accuracy of
Artificial Intelligence (AI) methods used for detection and grading of potentially malignant …
Artificial Intelligence (AI) methods used for detection and grading of potentially malignant …
Cellpose 2.0: how to train your own model
M Pachitariu, C Stringer - Nature methods, 2022 - nature.com
Pretrained neural network models for biological segmentation can provide good out-of-the-
box results for many image types. However, such models do not allow users to adapt the …
box results for many image types. However, such models do not allow users to adapt the …
Cellpose: a generalist algorithm for cellular segmentation
Many biological applications require the segmentation of cell bodies, membranes and nuclei
from microscopy images. Deep learning has enabled great progress on this problem, but …
from microscopy images. Deep learning has enabled great progress on this problem, but …
Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images
Nuclear segmentation and classification within Haematoxylin & Eosin stained histology
images is a fundamental prerequisite in the digital pathology work-flow. The development of …
images is a fundamental prerequisite in the digital pathology work-flow. The development of …
Lizard: A large-scale dataset for colonic nuclear instance segmentation and classification
S Graham, M Jahanifar, A Azam… - Proceedings of the …, 2021 - openaccess.thecvf.com
The development of deep segmentation models for computational pathology (CPath) can
help foster the investigation of interpretable morphological biomarkers. Yet, there is a major …
help foster the investigation of interpretable morphological biomarkers. Yet, there is a major …
Geospatial immune variability illuminates differential evolution of lung adenocarcinoma
Remarkable progress in molecular analyses has improved our understanding of the
evolution of cancer cells toward immune escape,,,–. However, the spatial configurations of …
evolution of cancer cells toward immune escape,,,–. However, the spatial configurations of …
Cross-patch dense contrastive learning for semi-supervised segmentation of cellular nuclei in histopathologic images
We study the semi-supervised learning problem, using a few labeled data and a large
amount of unlabeled data to train the network, by developing a cross-patch dense …
amount of unlabeled data to train the network, by developing a cross-patch dense …
NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images
The nuclei segmentation of hematoxylin and eosin (H&E) stained histopathology images is
an important prerequisite in designing a computer-aided diagnostics (CAD) system for …
an important prerequisite in designing a computer-aided diagnostics (CAD) system for …
[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 …