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Image Descriptors for Weakly Annotated Histopathological Breast Cancer Data
2020
Frontiers in Digital Health
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The WAID outperform all other descriptors on the Breast Cancer Histopathological database (BreakHis) where correctly classified malignant (CCM) instances reached 91.27 and 92.00% at the patient and image ...
In that direction, we derive the Weakly Annotated Image Descriptor (WAID) as the parameters of classifier decision boundaries in a Multiple Instance Learning framework. ...
DISCUSSION In this work, we presented a framework for the analysis of histopathological breast cancer data in the presence of weak supervision. ...
doi:10.3389/fdgth.2020.572671
pmid:33345255
pmcid:PMC7749086
fatcat:hcpbn7f72vb3firp24qnlh4pmy
Deep learning of feature representation with multiple instance learning for medical image analysis
2014
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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This paper studies the effectiveness of accomplishing high-level tasks with a minimum of manual annotation and good feature representations for medical images. ...
In medical image analysis, objects like cells are characterized by significant clinical features. ...
Annotations: Both annotations for fully supervised(cancer region) and weakly-supervised(bag labels) were labeled by two pathologists independently. ...
doi:10.1109/icassp.2014.6853873
dblp:conf/icassp/XuMFZLC14
fatcat:wrctt7kxlvhotoipewsw3nqmbm
Multi-view Attention-guided Multiple Instance Detection Network for Interpretable Breast Cancer Histopathological Image Diagnosis
2021
IEEE Access
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Deep learning approaches have demonstrated significant progress in breast cancer histopathological image diagnosis. ...
This transfers DML to a weakly supervised learning problem. Three public breast cancer histopathological image datasets are chosen to evaluate classification and localization results. ...
The authors also would like to thank the editor and the reviewers for their helpful suggestions. ...
doi:10.1109/access.2021.3084360
fatcat:2c3gh7c3enalld3ibmv7cuej3q
Fine-Grained Breast Cancer Classification With Bilinear Convolutional Neural Networks (BCNNs)
2020
Frontiers in Genetics
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In this study, we introduce a Bilinear Convolutional Neural Networks (BCNNs) based deep learning method for fine-grained classification of breast cancer histopathological images. ...
Classification of histopathological images of cancer is challenging even for well-trained professionals, due to the fine-grained variability of the disease. ...
Wang et al. (2017) used BCNNs for colorectal cancer histopathological image classification. ...
doi:10.3389/fgene.2020.547327
pmid:33101377
pmcid:PMC7500315
fatcat:mii32twm2japhkfpodunfpcchy
Deep Object Detection based Mitosis Analysis in Breast Cancer Histopathological Images
[article]
2020
arXiv
pre-print
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This work proposes an end-to-end detection system for mitotic nuclei identification in breast cancer histopathological images. ...
for the identification of other nuclear bodies in histopathological images. ...
Acknowledgements The authors thank Higher Education Commission of Pakistan (HEC) for granting funds under HEC indigenous scholarship program and Pattern Recognition lab at DCIS, PIEAS, for providing computational ...
arXiv:2003.08803v1
fatcat:bryz622fnba5bkgmyqpcapkxby
Deep Learning for Whole-Slide Tissue Histopathology Classification: A Comparative Study in the Identification of Dysplastic and Non-Dysplastic Barrett's Esophagus
2020
Journal of Personalized Medicine
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of esophageal cancer compared to weakly supervised and fully supervised approaches. ...
Deep learning-based approaches have shown promising results in the analysis of whole-slide tissue histopathology images (WSIs). ...
Multiple instance learning for
histopathological breast cancer image classification. Expert Syst. Appl. 2019, 117, 103–111. [CrossRef]
21. ...
doi:10.3390/jpm10040141
pmid:32977465
pmcid:PMC7711456
fatcat:j73keotvufgq7nja26vtlbqq7e
Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications
2022
Cancers
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We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. ...
of human cancers. ...
/vri/databases/breast-cancer- BACH 2018 Breast 500 histopathological-database-breakhis/ (accessed on 17 January 2022) Prognosis CRCHisto 2016 Colon 100 https://warwick.ac.uk/fac/cross_fac/tia/data/ crchistolabelednucleihe ...
doi:10.3390/cancers14051199
pmid:35267505
pmcid:PMC8909166
fatcat:7tfcfh4z45goxbcgf23sncok5a
CELNet: Evidence Localization for Pathology Images using Weakly Supervised Learning
[article]
2019
arXiv
pre-print
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and slide-level histopathologic cancer detection tasks. ...
To overcome this problem, we propose a weakly supervised learning-based approach that can effectively learn to localize the discriminative evidence for a diagnostic label from weakly labeled training data ...
evidence localization using weakly training data, which reduces the burden of
annotations. ...
arXiv:1909.07097v1
fatcat:jlkdftmq4ndqbiqgglbhsq2lxa
1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset
2018
GigaScience
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Conclusions: A unique dataset of annotated, whole-slide digital histopathology images has been provided with high potential for re-use. ...
Results: We released a dataset of 1,399 annotated whole-slide images (WSIs) of lymph nodes, both with and without metastases, in 3 terabytes of data in the context of the CAMELYON16 and CAMELYON17 Grand ...
Funding Data collection and annotation where funded by Stichting IT Projecten and by the Fonds Economische Structuurversterking (tEPIS/TRAIT project; LSH-FES Program 2009; DFES1029161 and FES1103JJTBU) ...
doi:10.1093/gigascience/giy065
pmid:29860392
pmcid:PMC6007545
fatcat:autvmenytjgv5ohswrpx75o5mu
Towards Large-Scale Histopathological Image Analysis: Hashing-Based Image Retrieval
2015
IEEE Transactions on Medical Imaging
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Recently, with the ever-increasing amount of annotated medical data, large-scale and data-driven methods have emerged to offer a promise of bridging the semantic gap between images and diagnostic information ...
Index Terms-Breast lesion, hashing, high dimension, histopathological image analysis, large-scale image retrieval, supervised learning. ...
Fortunately, over the past decade the death rate for female breast cancer has decreased by 17%, although the incidence of breast cancer has risen. ...
doi:10.1109/tmi.2014.2361481
pmid:25314696
fatcat:frt22wzviraffm7zjex5wgzlau
Artificial intelligence in breast ultrasound
2020
Ultrasonography
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Although breast ultrasonography is the mainstay modality for differentiating between benign and malignant breast masses, it has intrinsic problems with false positives and substantial interobserver variability ...
AI is highly useful for performing three main clinical tasks in breast ultrasonography: detection (localization/ segmentation), differential diagnosis (classification), and prognostication (prediction) ...
The lexicon includes descriptors for mass, orientation, margin, echo pattern, and posterior features for breast masses. ...
doi:10.14366/usg.20117
pmid:33430577
pmcid:PMC7994743
fatcat:s3qz3wpe5zbnxaexaeppk62q7u
Retrieval From and Understanding of Large-Scale Multi-modal Medical Datasets: A Review
2017
IEEE transactions on multimedia
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Annotated data sets and clinical data for the images have now become available and can be combined for multimodal retrieval. Much has been learned on user behavior and application scenarios. ...
This text is motivated by the advances in medical image analysis and the availability of more public data large data sets that often include clinical data that can be combined for multimodal retrieval ...
One tendency that was found in this article is that less supervised approaches are being developed, which can leverage large amounts of weakly annotated data or even data without annotations. ...
doi:10.1109/tmm.2017.2729400
fatcat:td4s7hbegzbmhlosalzlc3p7tq
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
As such, graph data representations and deep learning have attracted significant attention for encoding tissue representations, and capturing intra- and inter- entity level interactions. ...
and biopsy image patches. ...
Hierarchical cell-to-tissue graph representation for breast cancer. Images adapted from [14] - [16] . techniques. ...
arXiv:2107.00272v2
fatcat:3eskkeref5ccniqsjgo3hqv2sa
Image Features Based on Characteristic Curves and Local Binary Patterns for Automated HER2 Scoring
2018
Journal of Imaging
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This paper presents novel feature descriptors and classification algorithms for 9 automated scoring of HER2 in Whole Slide Images (WSI) of breast cancer histology slides. ...
Manual grading and annotations of breast cancer slides are time consuming, 33 and there are huge maintenance costs associated with collecting, archiving, and transporting tissue 34 specimens. ...
Introduction
27 The most commonly used method for breast cancer grading is the ImmunoHistoChemistry 28 (IHC) test which is a staining process performed on biopsy samples of breast cancer tissues [1] ...
doi:10.3390/jimaging4020035
fatcat:a3coihowy5gcbgdfxyfh5t3q2i
Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations
[article]
2020
arXiv
pre-print
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In this paper, we address the question of interpretability in the context of whole slide images (WSI) classification. ...
Deep learning methods are widely used for medical applications to assist medical doctors in their daily routines. ...
AUC of 0.775 for a breast cancer dataset and 0.968 for a colon cancer dataset were reported. Re-cently, more works on large datasets proposed architectures that follow the same design [11, 12] . ...
arXiv:2009.14001v1
fatcat:yriowecbive3jdopuaiknhm5dm
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