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Improving Interpretability for Computer-aided Diagnosis tools on Whole Slide Imaging with Multiple Instance Learning and Gradient-based Explanations [article]

Antoine Pirovano and Hippolyte Heuberger and Sylvain Berlemont and Saïd Ladjal and Isabelle Bloch
2020 arXiv   pre-print
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

Antoine Pirovano, Hippolyte Heuberger, Sylvain Berlemont, SaÏd Ladjal, Isabelle Bloch
2021 Machine Learning and Knowledge Extraction  
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]

Adriano Lucieri, Muhammad Naseer Bajwa, Andreas Dengel, Sheraz Ahmed
2020 arXiv   pre-print
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]

Magdalini Paschali, Muhammad Ferjad Naeem, Walter Simson, Katja Steiger, Martin Mollenhauer, Nassir Navab
2019 arXiv   pre-print
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]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
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]

Jiayun Li, Wenyuan Li, Anthony Sisk, Huihui Ye, W. Dean Wallace, William Speier, Corey W. Arnold
2020 arXiv   pre-print
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

Octavian Sabin Tătaru, Mihai Dorin Vartolomei, Jens J. Rassweiler, Oșan Virgil, Giuseppe Lucarelli, Francesco Porpiglia, Daniele Amparore, Matteo Manfredi, Giuseppe Carrieri, Ugo Falagario, Daniela Terracciano, Ottavio de Cobelli (+3 others)
2021 Diagnostics  
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]

Felipe Giuste, Wenqi Shi, Yuanda Zhu, Tarun Naren, Monica Isgut, Ying Sha, Li Tong, Mitali Gupte, May D. Wang
2021 arXiv   pre-print
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

William Huang, Chunli Zhao, Xiujun Fan
2020 International Journal of Innovative Research in Medical Science  
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]

Pengyi Zhang, Yunxin Zhong, Yulin Deng, Xiaoying Tang, Xiaoqiong Li
2019 arXiv   pre-print
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]

Georgios Sofianidis, Jože M. Rožanec, Dunja Mladenić, Dimosthenis Kyriazis
2021 arXiv   pre-print
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]

Di Jin and Elena Sergeeva and Wei-Hung Weng and Geeticka Chauhan and Peter Szolovits
2021 arXiv   pre-print
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]

Hao Jiang, Yanning Zhou, Yi Lin, Ronald CK Chan, Jiang Liu, Hao Chen
2022 arXiv   pre-print
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]

Meghna P Ayyar, Jenny Benois-Pineau, Akka Zemmari
2021 arXiv   pre-print
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

Teresa Conceição, Cristiana Braga, Luís Rosado, Maria João M. Vasconcelos
2019 International Journal of Molecular Sciences  
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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