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Deep Learning for Computational Cytology: A Survey
[article]
2022
arXiv
pre-print
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Computational cytology is a critical, rapid-developing, yet challenging topic in the field of medical image computing which analyzes the digitized cytology image by computer-aided technologies for cancer ...
We first introduce various deep learning methods, including fully supervised, weakly supervised, unsupervised, and transfer learning. ...
Acknowledgments This work was supported by Beijing Institute of Collaborative Innovation Program (No. BICI22EG01). ...
arXiv:2202.05126v2
fatcat:d5ockk4ofjgv3oyxnuce4hmxpu
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 ...
In this review, we provide a conceptual grounding for graph analytics in digital pathology, including entity-graph construction and graph architectures, and present their current success for tumor localization ...
Cervical cancer is one of the most common causes of cancer death in women, and screening for abnormal cells from a cervical cytology slide is a common procedure for early detection of cervical cancer. ...
arXiv:2107.00272v2
fatcat:3eskkeref5ccniqsjgo3hqv2sa
Deep Learning in Selected Cancers' Image Analysis—A Survey
2020
Journal of Imaging
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Deep learning has been applied in almost all of the imaging modalities used for cervical and breast cancers and MRIs for the brain tumor. ...
Moreover, the application of deep learning to imaging devices for the detection of various cancer cases has been studied by researchers affiliated to academic and medical institutes in economically developed ...
DCE) MRI data in a weakly supervised manner. ...
doi:10.3390/jimaging6110121
pmid:34460565
fatcat:2xvx5uya25a23nxicq3hdl42hi
Information Theoretic Clustering for Medical Image Segmentation
[chapter]
2013
Advanced Computational Approaches to Biomedical Engineering
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The effectiveness of this nonlinear approach is demonstrated in the segmentation of uterine cervix color images for early identification of cervical neoplasia, as an aid to cervical cancer diagnosis. ...
The limitations of this method in the segmentation of specific medical images such as brain images with multiple sclerosis lesions and a strategy to overcome them are discussed. ...
This technique could aid in the development of a diagnostic tool for cervical cancer. ...
doi:10.1007/978-3-642-41539-5_2
fatcat:5ghcmvm2rvg6xgungqxn7ccj2q
Medical image analysis based on deep learning approach
2021
Multimedia tools and applications
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Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions ...
Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision. ...
DLA is generally applicable for detecting an abnormality and classify a specific type of disease. ...
doi:10.1007/s11042-021-10707-4
pmid:33841033
pmcid:PMC8023554
fatcat:cm522go4nbdbnglgzpw4nu7tbi
Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body FDG-PET/CT
2021
Nature Communications
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Using these generated labels, we then train an attention-based, multi-task CNN architecture to detect and estimate the location of abnormalities in whole-body scans. ...
We demonstrate empirically that our multi-task representation is critical for strong performance on rare abnormalities with limited training data. ...
We have also released the parameters and raw outputs of our experiments at https://github.com/seyuboglu/weakly-supervised-petct. Source data are provided with this paper. ...
doi:10.1038/s41467-021-22018-1
pmid:33767174
fatcat:vko3koauunhgzodfxjjbek23gi
Progression of Abnormal MIB-1 Staining Patterns of Reserve Cells in Cervical Smears from Women Ultimately Developing High Grade Squamous Intraepithelial Lesions
2006
Acta Cytologica
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The Internet Archive has digitized a microfilm copy of this work. It may be possible to borrow a copy for reading.
Two pathologists reviewed abnormal cases from the rescreening process before a consensus diagnosis of ASC-US or a greater abnormality detected on re- screening were considered false negative results. ...
° This study examined the prevalence of HPV infection in Hong Kong since prevalence is important in formu- lating local strategy for cervical cancer screening. ...
doi:10.1159/000326033
fatcat:7eysa42qijhepbddg3hfafzc24
Deep learning in medical imaging and radiation therapy
2018
Medical Physics (Lancaster)
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that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. ...
for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions. ...
or multiple specific lung diseases and used weakly supervised learning to localize the region with the disease. ...
doi:10.1002/mp.13264
pmid:30367497
fatcat:bottst5mvrbkfedbuocbrstcnm
Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions
2021
Computational and Mathematical Methods in Medicine
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Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. ...
In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. ...
[129] used the idea of weakly supervised learning exploiting image-level labels for the classification of lung cancer images. ...
doi:10.1155/2021/9025470
pmid:34754327
pmcid:PMC8572604
fatcat:wgpostjgsfeijazpyguobcrx4i
Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images
2021
Diagnostics
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The use of "key" characteristics specific to each constituent of DL models and the correct determination of their correlations, may be the subject of future research, with the aim of increasing the performance ...
The need for time and attention, given by the doctor to the patient, due to the increased volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes has encouraged the ...
Acronyms: MRI Magnetic Resonance Images, CT Computed Tomography, SLO Scanning Laser Ophthalmoscopy images, X-ray on weakly-supervised classification and localization of common thorax diseases. ...
doi:10.3390/diagnostics11081373
fatcat:6p7usnvnxnewtivzeth745s3ga
MobileNetV2 Ensemble for Cervical Precancerous Lesions Classification
2020
Processes
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Together with HPV (Human Papillomavirus) testing and cytology, colposcopy has played a central role in cervical cancer screening. ...
These results are promising for the future use of automatic classification methods based on deep learning as tools to support medical doctors. ...
Furthermore, we believe that the use of the network activations' visualization could complement the usual network evaluation. This could help in targeting the problems of the model. ...
doi:10.3390/pr8050595
fatcat:3harm4xavnaf5aq4uvzs4o7j44
Abstracts from USCAP 2020: Informatics (1522-1590)
2020
Laboratory Investigation
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We sought to utilize whole slide quantitative image analysis (QIA) by HALO (Indica Labs, New Mexico, USA) to develop a screening protocol for organism detection with high sensitivity and negative predictive ...
Two-thirds of cases were referred by pathologists, and one third by radiologists. Clinical diagnosis concerned cancer in 94%. ...
DIA can provide an efficient and standardized approach for quantifying stromal CD8+ TIL density. ...
doi:10.1038/s41374-020-0393-8
pmid:32139865
fatcat:wecibsafmzhzjjgxqvxalr3xn4
A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis
[article]
2021
arXiv
pre-print
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To boost the accuracy and objectivity of detection, nowadays, an increasing number of computer-aided diagnosis (CAD) system is proposed. ...
Pathology image analysis is an essential procedure for clinical diagnosis of many diseases. ...
N2019003) and the "China Scholarship Council" (No. 2018GBJ001757). We thank Miss Zixian Li and Mr. Guoxian Li for their importantsupport and discussion in this work. ...
arXiv:2009.13721v3
fatcat:q46wb3rhwjcode3b46h6v2lhoa
7thESACP Congress in Caen April 1–5, 2001
2001
Analytical Cellular Pathology
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This approach could be used as a cancer screening tool in risk groups and as a monitor device of patient treatment. ...
Multi-colour COBRA FISH was applied to detect cryptic translocations and abnormalities in patients with abnormal phenotype but normal Giemsa karyotype, to study HPV 16 integration sites in cervical cancer ...
Aims: To develop a new type of didactic tool as a combination of computer based teaching and learning multimedia software with a knowledge based diagnostic system in cytopathology of effusions. ...
doi:10.1155/2001/414753
fatcat:7vxekd2dbjherbnpv5q7dshpsu
Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions
[article]
2019
arXiv
pre-print
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We draw on the insights from the sister research fields of Computer Vision, Pattern Recognition and Machine Learning etc.; where the techniques of dealing with such challenges have already matured, to ...
This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community. ...
The authors
used CNN with 169-layers for the detection of normality
and abnormality in each image study. ...
arXiv:1902.05655v1
fatcat:mjplenjrprgavmy5ssniji4cam
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