Addressing caveats of neural persistence with deep graph persistence
Neural Persistence is a prominent measure for quantifying neural network complexity,
proposed in the emerging field of topological data analysis in deep learning. In this work …
proposed in the emerging field of topological data analysis in deep learning. In this work …
Mudslide: A Universal Nuclear Instance Segmentation Method
J Wang - Proceedings of the IEEE/CVF Conference on …, 2024 - openaccess.thecvf.com
Nuclear instance segmentation has played a critical role in pathology image analysis. The
main challenges arise from the difficulty in accurately segmenting densely overlapping …
main challenges arise from the difficulty in accurately segmenting densely overlapping …
Unleashing the Power of Prompt-driven Nucleus Instance Segmentation
Nuclear instance segmentation in histology images is crucial for a broad spectrum of clinical
applications. Current prevailing nuclear instance segmentation algorithms rely on …
applications. Current prevailing nuclear instance segmentation algorithms rely on …
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation
Y Ming, Z Wu, J Yang, D Li, Y Gao, C Gao… - arXiv preprint arXiv …, 2024 - arxiv.org
Nucleus instance segmentation from histopathology images suffers from the extremely
laborious and expert-dependent annotation of nucleus instances. As a promising solution to …
laborious and expert-dependent annotation of nucleus instances. As a promising solution to …
TopoSemiSeg: Enforcing Topological Consistency for Semi-Supervised Segmentation of Histopathology Images
In computational pathology, segmenting densely distributed objects like glands and nuclei is
crucial for downstream analysis. To alleviate the burden of obtaining pixel-wise annotations …
crucial for downstream analysis. To alleviate the burden of obtaining pixel-wise annotations …
Revisiting Adaptive Cellular Recognition Under Domain Shifts: A Contextual Correspondence View
J Fan, D Liu, C Li, H Chang, H Huang, F Braet… - arXiv preprint arXiv …, 2024 - arxiv.org
Cellular nuclei recognition serves as a fundamental and essential step in the workflow of
digital pathology. However, with disparate source organs and staining procedures among …
digital pathology. However, with disparate source organs and staining procedures among …
UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images
In digital pathology, precise nuclei segmentation is pivotal yet challenged by the diversity of
tissue types, staining protocols, and imaging conditions. Recently, the segment anything …
tissue types, staining protocols, and imaging conditions. Recently, the segment anything …
Semantic and Instance Segmentation of Multi-organ Cell Nuclei Using Deep Learning Based Methods
In this paper, a semantic and instance-based segmentation study for cell nuclei in multi-
organ histology images is presented. In the proposed study, it was aimed to segment the cell …
organ histology images is presented. In the proposed study, it was aimed to segment the cell …
Multi-Task Learning with Graph-Guided Feature Fusion Module for Nuclear Instance Segmentation
H Mei, Y Zhong, J Fan - 2024 7th International Conference on …, 2024 - ieeexplore.ieee.org
Nuclear instance segmentation provides crucial nucleus morphology information for
downstream computational pathology tasks, such as disease diagnosis and treatment …
downstream computational pathology tasks, such as disease diagnosis and treatment …