Breast cancer detection, segmentation and classification on histopathology images analysis: a systematic review
R Krithiga, P Geetha - Archives of Computational Methods in Engineering, 2021 - Springer
Digital pathology represents a major evolution in modern medicine. Pathological
examinations constitute the standard in medical protocols and the law, and call for specific …
examinations constitute the standard in medical protocols and the law, and call for specific …
Generative adversarial networks in digital pathology: a survey on trends and future potential
Image analysis in the field of digital pathology has recently gained increased popularity. The
use of high-quality whole-slide scanners enables the fast acquisition of large amounts of …
use of high-quality whole-slide scanners enables the fast acquisition of large amounts of …
DenseRes-Unet: Segmentation of overlapped/clustered nuclei from multi organ histopathology images
Cancer is the second deadliest disease globally that can affect any human body organ.
Early detection of cancer can increase the chances of survival in humans. Morphometric …
Early detection of cancer can increase the chances of survival in humans. Morphometric …
A survey on recent trends in deep learning for nucleus segmentation from histopathology images
Nucleus segmentation is an imperative step in the qualitative study of imaging datasets,
considered as an intricate task in histopathology image analysis. Segmenting a nucleus is …
considered as an intricate task in histopathology image analysis. Segmenting a nucleus is …
High resolution spatial profiling of kidney injury and repair using RNA hybridization-based in situ sequencing
H Wu, EE Dixon, Q Xuanyuan, J Guo… - Nature …, 2024 - nature.com
Emerging spatially resolved transcriptomics technologies allow for the measurement of gene
expression in situ at cellular resolution. We apply direct RNA hybridization-based in situ …
expression in situ at cellular resolution. We apply direct RNA hybridization-based in situ …
Evaluation of deep learning architectures for complex immunofluorescence nuclear image segmentation
Separating and labeling each nuclear instance (instance-aware segmentation) is the key
challenge in nuclear image segmentation. Deep Convolutional Neural Networks have been …
challenge in nuclear image segmentation. Deep Convolutional Neural Networks have been …
NuSeT: A deep learning tool for reliably separating and analyzing crowded cells
Segmenting cell nuclei within microscopy images is a ubiquitous task in biological research
and clinical applications. Unfortunately, segmenting low-contrast overlapping objects that …
and clinical applications. Unfortunately, segmenting low-contrast overlapping objects that …
Intratumoral injection of hydrogel-embedded nanoparticles enhances retention in glioblastoma
Intratumoral drug delivery is a promising approach for the treatment of glioblastoma
multiforme (GBM). However, drug washout remains a major challenge in GBM therapy. Our …
multiforme (GBM). However, drug washout remains a major challenge in GBM therapy. Our …
3-D inorganic crystal structure generation and property prediction via representation learning
Generative models have been successfully used to synthesize completely novel images,
text, music, and speech. As such, they present an exciting opportunity for the design of new …
text, music, and speech. As such, they present an exciting opportunity for the design of new …
Explainable synthetic image generation to improve risk assessment of rare pediatric heart transplant rejection
Expert microscopic analysis of cells obtained from frequent heart biopsies is vital for early
detection of pediatric heart transplant rejection to prevent heart failure. Detection of this rare …
detection of pediatric heart transplant rejection to prevent heart failure. Detection of this rare …