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Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations
[article]
2020
arXiv
pre-print
Preserved Fulltext
computing interpretability slide-level heat-maps, based on the extracted features, that improves tile-level classification performances by more than 29% for AUC. ...
We formalize the design of WSI classification architectures and propose a piece-wise interpretability approach, relying on gradient-based methods, feature visualization and multiple instance learning context ...
Along with these new methods, the recent emergence of Whole Slide Imaging (WSI), microscopy slides digitized at a high resolution, represents a real opportunity for the development of efficient Computer-Aided ...
arXiv:2009.14001v1
fatcat:yriowecbive3jdopuaiknhm5dm
Automatic Feature Selection for Improved Interpretability on Whole Slide Imaging
2021
Machine Learning and Knowledge Extraction
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a piece-wise interpretability approach, relying on gradient-based methods, feature visualization and multiple instance learning context. ...
computing interpretability slide-level heat-maps, based on the extracted features, that improves tile-level classification performances. ...
Further validation of heat-map improvement using a ROAR approach adapted for Multiple Instance Learning (MIL) context; 2. ...
doi:10.3390/make3010012
fatcat:7jcmnkajmvc4tmkxfrstk77hte
Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis Systems
[article]
2020
arXiv
pre-print
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Especially in the medical context where Computer-Aided Diagnosis can have a direct influence on the treatment and well-being of patients, transparency is of utmost importance for safe transition from lab ...
This paper provides a comprehensive overview of current state-of-the-art in explaining and interpreting Deep Learning based algorithms in applications of medical research and diagnosis of diseases. ...
These factors, among others, contributed to current trend in the field of AI-based diagnosis to move towards Computer-Aided Diagnosis (CAD) and so called "Augmented Doctor" [13] . ...
arXiv:2011.13169v1
fatcat:cwj5dirccnb4dp3neym3krwvk4
Deep Learning Under the Microscope: Improving the Interpretability of Medical Imaging Neural Networks
[article]
2019
arXiv
pre-print
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A Deep Neural Network (DNN), inspired by Bag-of-Features models is equipped with a Multiple Instance Learning (MIL) branch and trained with weak supervision for WSI classification. ...
In this paper, we propose a novel interpretation method tailored to histological Whole Slide Image (WSI) processing. ...
Developing interpretable DNNs that provide comprehensive explanations for their decisions would enable their full integration to Computer Aided Diagnosis (CAD) Systems to assist physicians and alleviate ...
arXiv:1904.03127v2
fatcat:b5piii7f25holnwqo4z6wrul54
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 ...
We provide an overview of these methods in a systematic manner organized by the graph representation of the input image, scale, and organ on which they operate. ...
Each graph with nodes representing different tissues serves as an instance, and the multiple instances for a WSI form a bag that aids in tumour stage prediction. ...
arXiv:2107.00272v2
fatcat:3eskkeref5ccniqsjgo3hqv2sa
A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning
[article]
2020
arXiv
pre-print
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Large numbers of histopathological images have been digitized into high resolution whole slide images, opening opportunities in developing computational image analysis tools to reduce pathologists' workload ...
In this paper, we proposed a multi-resolution multiple instance learning model that leverages saliency maps to detect suspicious regions for fine-grained grade prediction. ...
Therefore, the current clinical practice can be improved by computer aided diagnosis tools (CAD) that can function as primary screening, to localize suspicious regions, and be utilized as a second reader ...
arXiv:2011.02679v1
fatcat:fpuofo3ozna2pgzwazeeylql5u
Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management—Current Trends and Future Perspectives
2021
Diagnostics
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These technologies could provide doctors with better insights on how to plan radiotherapy treatment. ...
The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. ...
Article/Reference
Image Type
Image Analysis Method
Number of Slides or
Patients (N)
Task
Results
Campanella et al. [31]
Whole slide images
Multiple instances learning
based, deep learning
N ...
doi:10.3390/diagnostics11020354
pmid:33672608
pmcid:PMC7924061
fatcat:jqktyzjrhjh2jaxvlpk3pomube
Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2112.12705v1 fatcat:gsvyasf3qvc7rnduhumueeamvm
We hope this review may serve as a guide to improve the clinical impact of future AI-based solutions. ...
We find that successful use of XAI can improve model performance, instill trust in the end-user, and provide the value needed to affect user decision-making. ...
Kristan Majors, for her support and guidance on search optimization for the PRISMA chart. We would like to thank Dr. ...
arXiv:2112.12705v2
fatcat:pji2saeikbeq7phmygphbomm5e
Clinical Applications of Artificial Intelligence on Accuracy of Cancer Prediction, Detection, and Diagnosis
2020
International Journal of Innovative Research in Medical Science
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In particular, the ability for deep learning machines to determine the risk, survivability, and prognosis of tumors based on medical cancer databases has intrigued healthcare researchers seeking to improve ...
, and prediction based on patient information from available medical databases. ...
., C.Z. and X.
Funding This research received no external funding.
Conflicts of Interest The authors declare no conflict of interest. ...
doi:10.23958/ijirms/vol05-i10/978
fatcat:5o5qdykez5bondyrxpwlnd2qbe
A Survey on Deep Learning of Small Sample in Biomedical Image Analysis
[article]
2019
arXiv
pre-print
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The success of deep learning has been witnessed as a promising technique for computer-aided biomedical image analysis, due to end-to-end learning framework and availability of large-scale labelled samples ...
In order to accelerate the clinical usage of biomedical image analysis based on deep learning techniques, we intentionally expand this survey to include the explanation methods for deep models that are ...
Acknowledgements The authors would like to thank members of the Medical Image Analysis for discussions and suggestions. ...
arXiv:1908.00473v1
fatcat:atotvdxp6janve2mz3swyf47xa
A Review of Explainable Artificial Intelligence in Manufacturing
[article]
2021
arXiv
pre-print
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as deep learning and reinforcement learning techniques. ...
The implementation of Artificial Intelligence (AI) systems in the manufacturing domain enables higher production efficiency, outstanding performance, and safer operations, leveraging powerful tools such ...
Acknowledgements This work has been carried out in the H STAR project, which has received funding from the European Union's Horizon research and innovation programme under grant agreement No. . ...
arXiv:2107.02295v1
fatcat:hpnsn5l6jffrvdnjtufw6ogpsq
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View
[article]
2021
arXiv
pre-print
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DL based clinical decision support systems for diagnosis, prognosis, and treatment. ...
In this review, we focus on the interpretability of the DL models in healthcare. ...
In Medical Imaging 2020: Computer-Aided Diagnosis, volume 11314, page 113140Z.
International Society for Optics and Photonics. ...
arXiv:2112.02625v1
fatcat:omcm44vj2ffthcpna27typyvau
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 ...
To investigate the advanced methods and comprehensive applications, we survey more than 120 publications of DL-based cytology image analysis in this article. ...
For example, Pirovano et al. (2021) proposed a computer-aided diagnosis tool for cervical cancer screening. ...
arXiv:2202.05126v2
fatcat:d5ockk4ofjgv3oyxnuce4hmxpu
White Box Methods for Explanations of Convolutional Neural Networks in Image Classification Tasks
[article]
2021
arXiv
pre-print
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Given the task of image classification and a trained CNN, this work aims to provide a comprehensive and detailed overview of a set of methods that can be used to create explanation maps for a particular ...
In recent years, deep learning has become prevalent to solve applications from multiple domains. ...
SmoothGrad and Integrated Gradients computed explanation maps of gradient backpropagation over multiple variations of the input image and combined them to produce visualizations with reduced noise. ...
arXiv:2104.02548v2
fatcat:h3odimfjgnbdphtwgqsoclttmu
A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification
2019
International Journal of Molecular Sciences
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the next generation of computer-aided diagnosis systems and future research directions. ...
The increasing interest in the development of computer-aided solutions for cervical cancer screening is to aid with these common practical difficulties, which are especially frequent in the low-income ...
Fusing multimodal information, for instance textual and image data, can potentially improve the diagnosis performance. ...
doi:10.3390/ijms20205114
pmid:31618951
pmcid:PMC6834130
fatcat:vdenllm4kvb7bcpvaktfg5etma
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