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Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images
[article]
2022
arXiv
pre-print
Preserved Fulltext
To this end, we propose a self-supervised learning based approach with a Prior Self-activation Module (PSM) that generates self-activation maps from the input images to avoid labeling costs and further ...
Furthermore, a two-stage training module, consisting of a nuclei detection network and a nuclei segmentation network, is adopted to achieve the final segmentation. ...
Conclusion In this paper, we proposed a self-activation map based framework for unsupervised nuclei detection and segmentation. ...
arXiv:2210.07862v1
fatcat:z7iftuii6rcrvcdfos233y433a
Unsupervised Data-Driven Nuclei Segmentation For Histology Images
[article]
2021
arXiv
pre-print
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An unsupervised data-driven nuclei segmentation method for histology images, called CBM, is proposed in this work. ...
priors with morphological processing. ...
CONCLUSION AND FUTURE WORK Nuclei segmentation in histology images is a demanding and prone to errors task for physicians, and its automation is of high importance for cancer assessment. ...
arXiv:2110.07147v1
fatcat:dqljx2wiurfohlbu7x3heg42pq
Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images
[article]
2017
arXiv
pre-print
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Our CAE detects and encodes nuclei in image patches in tissue images into sparse feature maps that encode both the location and appearance of nuclei. ...
In this work, we propose a sparse Convolutional Autoencoder (CAE) for fully unsupervised, simultaneous nucleus detection and feature extraction in histopathology tissue images. ...
We propose a CAE architecture with crosswise sparsity that can detect and represent nuclei in histopathology images with the following advantages: • As far as we know, this is the first unsupervised detection ...
arXiv:1704.00406v2
fatcat:lp3yfks4fndm7dku62qvh5sale
Nuclei Glands Instance Segmentation in Histology Images: A Narrative Review
[article]
2022
arXiv
pre-print
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Instance segmentation of nuclei and glands in the histology images is an important step in computational pathology workflow for cancer diagnosis, treatment planning and survival analysis. ...
To the best of our knowledge, no previous work has reviewed the instance segmentation in histology images focusing towards this direction. ...
In this technique initial learnable layers, learns from prior information (generated edge map through raw input image and predefined shapes) via fixed processing and performs nuclei detection consistent ...
arXiv:2208.12460v1
fatcat:drl5p5cxtbadpcpjzoboxdxgnm
Deep neural network models for computational histopathology: A survey
[article]
2019
arXiv
pre-print
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Recently, deep learning has become the mainstream methodological choice for analyzing and interpreting cancer histology images. ...
Finally, we summarize several existing open datasets and highlight critical challenges and limitations with current deep learning approaches, along with possible avenues for future research. ...
, unsupervised and transfer learning) for a wide variety of histology tasks (e.g., cell or nuclei segmentation, tissue classification, tumour detection, disease prediction and prognosis), and has been ...
arXiv:1912.12378v1
fatcat:xdfkzzwzb5alhjfhffqpcurb2u
Synthetic Privileged Information Enhances Medical Image Representation Learning
[article]
2024
arXiv
pre-print
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Multimodal self-supervised representation learning has consistently proven to be a highly effective method in medical image analysis, offering strong task performance and producing biologically informed ...
In contrast, image generation methods can work well on very small datasets, and can find mappings between unpaired datasets, meaning an effectively unlimited amount of paired synthetic data can be generated ...
Edwards and the Glasgow Tissue Research Facility. ...
arXiv:2403.05220v1
fatcat:yrnm4q2xgrac3mnv7jx4hvmbke
A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
[chapter]
2018
Lecture Notes in Computer Science
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Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential 427 Deep Active Self-paced Learning for Accurate Pulmonary Nodule Segmentation 428 Deep Attentional Features for Prostate Segmentation ...
of regional brain activity for resting-state fMRI: d-ALFF, d-fALFF and d-ReHo 765 Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction 768 Accurate ...
doi:10.1007/978-3-030-00931-1_48
pmid:30338317
pmcid:PMC6191198
fatcat:dqhvpm5xzrdqhglrfftig3qejq
Deep Learning Models for Digital Pathology
[article]
2019
arXiv
pre-print
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the predictive modeling of histopathology images from a detection, stain normalization, segmentation, and tissue classification perspective. ...
However digitized histopathology tissue slides are unique in a variety of ways and come with their own set of computational challenges. ...
Another motivation for detecting and segmenting histologic primitives arises from the need for counting of objects, generally cells or nuclei. ...
arXiv:1910.12329v2
fatcat:2b7h7i2zwbautewneabghm3bzi
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
[article]
2024
arXiv
pre-print
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Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging. ...
The major deep learning methods and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are elaborated and discussed. ...
by leveraging the prior knowledge of nuclei size and quantity for nuclei segmentation. [230] SE Weakly supervised cell segmentation by generating reliable pseudo labels from scribbles. [504] SE Self-supervised ...
arXiv:2304.06662v4
fatcat:t5nvpybawjhfhiw4h2bekozo74
Study of Computerized Segmentation & Classification Techniques: An Application to Histopathological Imaginary
2019
Informatica (Ljubljana, Tiskana izd.)
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The main goal of this study is to understand and address the challenges associated with the development of image analysis techniques for computer-aided interpretation of histopathology imagery. ...
This paper reviews recent state of the art technology for histopathology and briefly describes the recent development in histology and its application towards quantifying the perceptive issue in the domain ...
Another motivation for detecting and segmenting histological structures has to do with the need for counting of objects, generally cells or cell nuclei. ...
doi:10.31449/inf.v43i4.2142
fatcat:wkqlu2h6fjcuxckqltjrtpkgai
Deep Learning in Image Cytometry: A Review
2018
Cytometry Part A
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for extracting information from image data. ...
In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. ...
Ewert Bengtsson, and Petter Ranefall for their appreciative suggestions.
LITERATURE CITED ...
doi:10.1002/cyto.a.23701
pmid:30565841
pmcid:PMC6590257
fatcat:dszbcsfncrhxnazsxopjkbe3ju
Discriminative Pattern Mining for Breast Cancer Histopathology Image Classification via Fully Convolutional Autoencoder
[article]
2019
arXiv
pre-print
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With minimum annotation information, the proposed method mines contrast patterns between normal and malignant images in unsupervised manner and generates a probability map of abnormalities to verify its ...
In this paper, we propose a practical and self-interpretable invasive cancer diagnosis solution. ...
Second, the proposed method detects discriminative patterns in images in unsupervised manner. ...
arXiv:1902.08670v2
fatcat:7jtvmweob5d2jihwsin7bv4sqy
Scale dependant layer for self-supervised nuclei encoding
[article]
2022
arXiv
pre-print
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In addition, we extend the existing TNBC dataset to incorporate nuclei class annotation in order to enrich and publicly release a small sample setting dataset for nuclei segmentation and classification ...
In the present paper, the focus lays in the nuclei in histopathology images. In particular we aim at extracting cellular information in an unsupervised manner for a downstream task. ...
Thomas Walter for his support in the project and in particular for the insightful discussions, help with the annotations and use of the software. ...
arXiv:2207.10950v1
fatcat:f7rnlcb7svc2picorkm3klv7pu
A Survey on Graph-Based Deep Learning for Computational Histopathology
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2107.00272v1 fatcat:hrphnxpyd5goxkghksu4zqghfm
With the remarkable success of representation learning for prediction problems, we have witnessed a rapid expansion of the use of machine learning and deep learning for the analysis of digital pathology ...
and biopsy image patches. ...
The authors segmented the nuclei and construct a cell-graph for each image with nuclei as the nodes, and the distance between neighboring nuclei as the edges, as illustrated in Fig. 6 . ...
arXiv:2107.00272v2
fatcat:3eskkeref5ccniqsjgo3hqv2sa
Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review
2016
IEEE Reviews in Biomedical Engineering
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In addition, we discuss the challenges for the current methods and the potential future work of nucleus/cell detection and segmentation. ...
Digital pathology and microscopy image analysis is widely used for comprehensive studies of cell morphology or tissue structure. ...
Another learning based method with shape prior modeling is presented in [40] for Pap smear nuclei segmentation, which combines the physical deformable model [255] and the active shape model (ASM) ...
doi:10.1109/rbme.2016.2515127
pmid:26742143
pmcid:PMC5233461
fatcat:hx5ldvsppvgzxk6rdiok7siyvi
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