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Papers on “artificial intelligence deep learning medical diagnosis imaging”

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1. A survey on deep learning in medical image analysis

   Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, et al. · 2017 · Medical Image Analysis · 15,110 cites

2. A survey on Image Data Augmentation for Deep Learning

   Connor Shorten, Taghi M. Khoshgoftaar · 2019 · Journal Of Big Data · 13,003 cites

   Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data. Unfortunately, many application domains do not have access to big data, such as medical image analysis. This survey focuses on Data Augmentation, a data-space solution to the problem of limited data. Data Augmentation encompasses a suite of techniques that enhance the size and quality of training datasets such that better Deep Learning models can be built using them. The image augme

3. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

   Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, et al. · 2019 · Information Fusion · 9,796 cites

4. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

   Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi, et al. · 2021 · Journal Of Big Data · 7,850 cites

   In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natur

5. Artificial intelligence in healthcare: past, present and future

   Fei Jiang, Yong Jiang, Hui Zhi, et al. · 2017 · Stroke and Vascular Neurology · 4,841 cites

   Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. W

6. The potential for artificial intelligence in healthcare

   Thomas H. Davenport, Ravi Kalakota · 2019 · Future Healthcare Journal · 3,814 cites

   The complexity and rise of data in healthcare means that artificial intelligence (AI) will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care, and life sciences companies. The key categories of applications involve diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also discussed.

7. A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

   Erico Tjoa, Cuntai Guan · 2020 · IEEE Transactions on Neural Networks and Learning Systems · 2,377 cites

   Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning (DL). Along with research progress, they have encroached upon many different fields and disciplines. Some of them require high level of accountability and thus transparency, for example, the medical sector. Explanations for machine decisions and predictions are thus needed to justify their reliability. This requires greater interpretability, which often means we need to understand the mechanism underlying the algorithms. Unfortunately, the blackbox nature of the DL is still

8. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

   Bas H. M. van der Velden, Hugo J. Kuijf, Kenneth G. A. Gilhuijs, et al. · 2022 · Medical Image Analysis · 1,282 cites

   With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of explainable artificial intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.