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Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images

Hui Qu, Pengxiang Wu, Qiaoying Huang, Jingru Yi, Zhennan Yan, Kang Li, Gregory M. Riedlinger, Subhajyoti De, Shaoting Zhang, Dimitris N. Metaxas
2020 IEEE Transactions on Medical Imaging  
Nuclei segmentation is a fundamental task in histopathology image analysis.  ...  To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i.e., only a small portion of nuclei locations  ...  In this paper, we describe a novel weakly supervised nuclei segmentation framework for histopathology images using only a portion of annotated nuclear locations.  ... 
doi:10.1109/tmi.2020.3002244 pmid:32746112 fatcat:qymqn4az4zdctn55o7f73xelli

Local Integral Regression Network for Cell Nuclei Detection

Xiao Zhou, Miao Gu, Zhen Cheng
2021 Entropy  
Nuclei detection is a fundamental task in the field of histopathology image analysis and remains challenging due to cellular heterogeneity.  ...  To alleviate such tedious effort and reduce the annotation cost, we propose a novel local integral regression network (LIRNet) that allows both fully and weakly supervised learning (FSL/WSL) frameworks  ...  We are deeply grateful to the reviewers for providing quite valuable comments and suggestions, which helped us to improve the quality of our manuscript.  ... 
doi:10.3390/e23101336 pmid:34682060 fatcat:m3bdo7kirzam7pgqvn6lgzehsm

Weakly Supervised Nuclei Segmentation via Instance Learning [article]

Weizhen Liu, Qian He, Xuming He
2022 arXiv   pre-print
Weakly supervised nuclei segmentation is a critical problem for pathological image analysis and greatly benefits the community due to the significant reduction of labeling cost.  ...  Adopting point annotations, previous methods mostly rely on less expressive representations for nuclei instances and thus have difficulty in handling crowded nuclei.  ...  This research study was conducted retrospectively using human subject data made available in open access by [15] and [16] .  ... 
arXiv:2202.01564v2 fatcat:2vnfd5szlraovowsuhvpzejnja

Weakly Supervised Deep Instance Nuclei Detection using Points Annotation in 3D Cardiovascular Immunofluorescent Images [article]

Nazanin Moradinasab, Yash Sharma, Laura S. Shankman, Gary K. Owens, Donald E. Brown
2022 arXiv   pre-print
In this study, we used a weakly supervised learning approach to train the HoVer-Net segmentation model using point annotations to detect nuclei in fluorescent images.  ...  To train the HoVer-Net model using point annotations, we adopted a popularly used cluster labeling approach to transform point annotations into accurate binary masks of cell nuclei.  ...  Also, this work was provided partially by a grant to the integrated Translational Health Research Institute (iTHRIV) with funding support from National Center for Advancing Translational Sciences (NCATS  ... 
arXiv:2208.00098v1 fatcat:7qqplxvovrbbfku6wmwyqhnyee

Deep neural network models for computational histopathology: A survey [article]

Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel
2019 arXiv   pre-print
In this paper, we present a comprehensive review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis.  ...  Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to diseases progression and patient survival outcomes.  ...  Not all methods identified as weakly supervised in the literature necessarily fall under the MIL category. to indicate that the model training has been performed on sparse set of annotations such as points  ... 
arXiv:1912.12378v1 fatcat:xdfkzzwzb5alhjfhffqpcurb2u

Promising Deep Semantic Nuclei Segmentation Models for Multi-Institutional Histopathology Images of Different Organs

Loay Hassan, Adel Saleh, Mohamed Abdel-Nasser, Osama A. Omer, Domenec Puig
2020 International Journal of Interactive Multimedia and Artificial Intelligence  
However, existing nuclei segmentation methods face many challenges, such as color variation in histopathological images, the overlapping and clumped nuclei, and the ambiguous boundary between different  ...  Nuclei segmentation in whole-slide imaging (WSI) plays a crucial role in the field of computational pathology.  ...  In [11] , Qu et al. proposed a weakly supervised segmentation framework based on partial points annotation in histopathology images.  ... 
doi:10.9781/ijimai.2020.10.004 fatcat:mhvhdy4xvbeejpfvd5f2ghs3aa

Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images

Yasmine Abu Haeyeh, Mohammed Ghazal, Ayman El-Baz, Iman M. Talaat
2022 Bioengineering  
We propose a novel multiscale weakly-supervised deep learning approach for RCC subtyping.  ...  The manual analysis of whole slide images (WSI) of renal tissues is the primary tool for RCC diagnosis and prognosis.  ...  Acknowledgments: We would like to thank Louisville University-Bioimaging Lab, supervised by Ayman El-Baz, for their contribution by providing us with the renal cancer dataset.  ... 
doi:10.3390/bioengineering9090423 pmid:36134972 pmcid:PMC9495730 fatcat:pbpsucuf4ja4vmdgga5d5seyua

PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge Distillation [article]

Shuwei Zhai, Guotai Wang, Xiangde Luo, Qiang Yue, Kang Li, Shaoting Zhang
2022 arXiv   pre-print
In this paper, we propose to annotate a segmentation target with only seven points in 3D medical images, and design a two-stage weakly supervised learning framework PA-Seg.  ...  The success of Convolutional Neural Networks (CNNs) in 3D medical image segmentation relies on massive fully annotated 3D volumes for training that are time-consuming and labor-intensive to acquire.  ...  [26] utilized Voronoi diagram and clustering to expand point annotations to pixel-level labels, and combined them with CRF loss for weakly supervised nuclei segmentation from histopathology images.  ... 
arXiv:2208.05669v1 fatcat:bzmjzvq4s5c6pcvrokmt6oijrm

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models

Jialin Peng, Ye Wang
2021 IEEE Access  
INDEX TERMS Medical image segmentation, semi-supervised segmentation, partially-supervised segmentation, noisy label, sparse annotation. 36828  ...  application of deep learning models in medical image segmentation.  ...  [263] investigated nuclei segmentation with point annotations.  ... 
doi:10.1109/access.2021.3062380 fatcat:r5vsec2yfzcy5nk7wusiftyayu

NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images [article]

Navid Alemi Koohbanani, Mostafa Jahanifar, Neda Zamani Tajadin, Nasir Rajpoot
2020 arXiv   pre-print
We show that for nuclei and cells in histology and cytology images, one click inside each object is enough for NuClick to yield a precise annotation.  ...  As exemplar outputs of our framework, we are releasing two datasets: 1) a dataset of lymphocyte annotations within IHC images, and 2) a dataset of segmented WBCs in blood smear images.  ...  For instance, [12] and [13] introduced weakly supervised nucleus segmentation models which are trained based on nuclei centroid points instead of full segmentation masks.  ... 
arXiv:2005.14511v2 fatcat:yg6wig3nzfg25lh2gz6xvgnqay

A review of machine learning approaches, challenges and prospects for computational tumor pathology [article]

Liangrui Pan, Zhichao Feng, Shaoliang Peng
2022 arXiv   pre-print
This review investigates image preprocessing methods in computational pathology from a pathological and technical perspective, machine learning-based methods, and applications of computational pathology  ...  better-integrated solutions for whole-slide images, multi-omics data, and clinical informatics.  ...  To reduce annotation time, a deep multi-magnification network (DMMN) is used to train using partially annotated images, followed by accurate multi-class tissue segmentation of the entire WSI [69] .  ... 
arXiv:2206.01728v1 fatcat:g7r7fsw2bzafpkkyg6hpzjyt5e

Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models [article]

Jialin Peng, Ye Wang
2021 arXiv   pre-print
application of deep learning models in medical image segmentation.  ...  The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling.  ...  [263] investigated nuclei segmentation with point annotations.  ... 
arXiv:2103.00429v1 fatcat:p44a5e34sre4nasea5kjvva55e

NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopic Images

Navid Alemi Koohbanani, Mostafa Jahanifar, Neda Zamani Tajadin, Nasir Rajpoot
2020 Medical Image Analysis  
We show that for nuclei and cells in histology and cytology images, one click inside each object is enough for NuClick to yield a precise annotation.  ...  As exemplar outputs of our framework, we are releasing two datasets: 1) a dataset of lymphocyte annotations within IHC images, and 2) a dataset of segmented WBCs in blood smear images.  ...  from histopathological images and then used the trained model to segment nuclei in cytology and immunohistochemistry (IHC) samples.  ... 
doi:10.1016/j.media.2020.101771 pmid:32769053 fatcat:gske27xgxvaynfkha7vuhqcwzu

Built to last? Reproducibility and Reusability of Deep Learning Algorithms in Computational Pathology [article]

Sophia J. Wagner, Christian Matek, Sayedali Shetab Boushehri, Melanie Boxberg, Lorenz Lamm, Ario Sadafi, Dominik J. E. Waibel, Carsten Marr, Tingying Peng
2022 medRxiv   pre-print
of computational pathology algorithms, we evaluated peer-reviewed articles available in Pubmed, published between January 2019 and March 2021, in five use cases: stain normalization, tissue type segmentation  ...  AbstractRecent progress in computational pathology has been driven by deep learning.  ...  workflow, and methods that use WSI-level annotations and are therefore weakly supervised.  ... 
doi:10.1101/2022.05.15.22275108 fatcat:wte5tu46gzf4pld25gbobqcjha

Deep Interactive Learning-based ovarian cancer segmentation of H E-stained whole slide images to study morphological patterns of BRCA mutation [article]

David Joon Ho, M. Herman Chui, Chad M. Vanderbilt, Jiwon Jung, Mark E. Robson, Chan-Sik Park, Jin Roh, Thomas J. Fuchs
2022 arXiv   pre-print
Deep learning has been widely used to analyze digitized hematoxylin and eosin (H&E)-stained histopathology whole slide images.  ...  In this paper, we propose Deep Interactive Learning with a pretrained segmentation model from a different cancer type to reduce manual annotation time.  ...  Weakly-supervised learning can be a promising solution for common cancers because it require a large training set representing one whole slide image as one data point.  ... 
arXiv:2203.15015v1 fatcat:7k72zbzfbfemxmj73txemt4sxa
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