Deep learning in breast cancer imaging: A decade of progress and future directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
In-situ multi-phase flow imaging for particle dynamic tracking and characterization: Advances and applications
Real-time chemical process monitoring, analysis, and control have become increasingly
important to multi-phase flow process research and development and attracted overt …
important to multi-phase flow process research and development and attracted overt …
Toposeg: Topology-aware nuclear instance segmentation
Nuclear instance segmentation has been critical for pathology image analysis in medical
science, eg, cancer diagnosis. Current methods typically adopt pixel-wise optimization for …
science, eg, cancer diagnosis. Current methods typically adopt pixel-wise optimization for …
Affine-consistent transformer for multi-class cell nuclei detection
Multi-class cell nuclei detection is a fundamental prerequisite in the diagnosis of
histopathology. It is critical to efficiently locate and identify cells with diverse morphology and …
histopathology. It is critical to efficiently locate and identify cells with diverse morphology and …
Nuclei segmentation using attention aware and adversarial networks
E Goceri - Neurocomputing, 2024 - Elsevier
Accurate segmentation of nuclei plays a critical role in pathology since assessments and
diagnoses are mainly based on the recognition, measurement, and counting of nuclei …
diagnoses are mainly based on the recognition, measurement, and counting of nuclei …
Learning to Generalize over Subpartitions for Heterogeneity-Aware Domain Adaptive Nuclei Segmentation
Annotation scarcity and cross-modality/stain data distribution shifts are two major obstacles
hindering the application of deep learning models for nuclei analysis, which holds a broad …
hindering the application of deep learning models for nuclei analysis, which holds a broad …
Attention guided multi-scale cluster refinement with extended field of view for amodal nuclei segmentation
M Luna, P Chikontwe, S Nam, SH Park - Computers in Biology and …, 2024 - Elsevier
Nuclei segmentation plays a crucial role in disease understanding and diagnosis. In whole
slide images, cell nuclei often appear overlapping and densely packed with ambiguous …
slide images, cell nuclei often appear overlapping and densely packed with ambiguous …
ConvNeXt-backbone HoVerNet for nuclei segmentation and classification
J Li, C Wang, B Huang, Z Zhou - arXiv preprint arXiv:2202.13560, 2022 - arxiv.org
This manuscript gives a brief description of the algorithm used to participate in CoNIC
Challenge 2022. After the baseline was made available, we follow the method in it and …
Challenge 2022. After the baseline was made available, we follow the method in it and …
Nuclei segmentation with point annotations from pathology images via self-supervised learning and co-training
Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology.
Generally, the segmentation performance of fully-supervised learning heavily depends on …
Generally, the segmentation performance of fully-supervised learning heavily depends on …
An imbalance-aware nuclei segmentation methodology for H&E stained histopathology images
A key step in computational pathology is to automate the laborious process of manual nuclei
segmentation in Hematoxylin and Eosin (H&E) stained whole slide images (WSIs). Despite …
segmentation in Hematoxylin and Eosin (H&E) stained whole slide images (WSIs). Despite …