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Register a new userExplainable artificial intelligence (XAI) in deep learning-based medical image analysis
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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.
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Medical imaging is widely used in cancer diagnosis and treatment, and artificial intelligence (AI) has achieved tremendous success in various tasks of medical image analysis. This paper reviews AI-based tumor subregion analysis in medical imaging. We summarize the latest AI-based methods for tumor subregion analysis and their applications. Specifically, we categorize the AI-based methods by training strategy: supervised and unsupervised. A detailed review of each category is presented, highlighting important contributions and achievements. Specific challenges and potential AI applications in tumor subregion analysis are discussed.
Background and Objective: Deep learning enables tremendous progress in medical image analysis. One driving force of this progress are open-source frameworks like TensorFlow and PyTorch. However, these frameworks rarely address issues specific to the domain of medical image analysis, such as 3-D data handling and distance metrics for evaluation. pymia, an open-source Python package, tries to address these issues by providing flexible data handling and evaluation independent of the deep learning framework. Methods: The pymia package provides data handling and evaluation functionalities. The data handling allows flexible medical image handling in every commonly used format (e.g., 2-D, 2.5-D, and 3-D; full- or patch-wise). Even data beyond images like demographics or clinical reports can easily be integrated into deep learning pipelines. The evaluation allows stand-alone result calculation and reporting, as well as performance monitoring during training using a vast amount of domain-specific metrics for segmentation, reconstruction, and regression. Results: The pymia package is highly flexible, allows for fast prototyping, and reduces the burden of implementing data handling routines and evaluation methods. While data handling and evaluation are independent of the deep learning framework used, they can easily be integrated into TensorFlow and PyTorch pipelines. The developed package was successfully used in a variety of research projects for segmentation, reconstruction, and regression. Conclusions: The pymia package fills the gap of current deep learning frameworks regarding data handling and evaluation in medical image analysis. It is available at https://github.com/rundherum/pymia and can directly be installed from the Python Package Index using pip install pymia.
Automated medical image segmentation is an important step in many medical procedures. Recently, deep learning networks have been widely used for various medical image segmentation tasks, with U-Net and generative adversarial nets (GANs) being some of the commonly used ones. Foreground-background class imbalance is a common occurrence in medical images, and U-Net has difficulty in handling class imbalance because of its cross entropy (CE) objective function. Similarly, GAN also suffers from class imbalance because the discriminator looks at the entire image to classify it as real or fake. Since the discriminator is essentially a deep learning classifier, it is incapable of correctly identifying minor changes in small structures. To address these issues, we propose a novel context based CE loss function for U-Net, and a novel architecture Seg-GLGAN. The context based CE is a linear combination of CE obtained over the entire image and its region of interest (ROI). In Seg-GLGAN, we introduce a novel context discriminator to which the entire image and its ROI are fed as input, thus enforcing local context. We conduct extensive experiments using two challenging unbalanced datasets: PROMISE12 and ACDC. We observe that segmentation results obtained from our methods give better segmentation metrics as compared to various baseline methods.
More recently, Explainable Artificial Intelligence (XAI) research has shifted to focus on a more pragmatic or naturalistic account of understanding, that is, whether the stakeholders understand the explanation. This point is especially important for research on evaluation methods for XAI systems. Thus, another direction where XAI research can benefit significantly from cognitive science and psychology research is ways to measure understanding of users, responses and attitudes. These measures can be used to quantify explanation quality and as feedback to the XAI system to improve the explanations. The current report aims to propose suitable metrics for evaluating XAI systems from the perspective of the cognitive states and processes of stakeholders. We elaborate on 7 dimensions, i.e., goodness, satisfaction, user understanding, curiosity & engagement, trust & reliance, controllability & interactivity, and learning curve & productivity, together with the recommended subjective and objective psychological measures. We then provide more details about how we can use the recommended measures to evaluate a visual classification XAI system according to the recommended cognitive metrics.
Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. In this paper, we present a comprehensive thematic survey on medical image segmentation using deep learning techniques. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi-level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyze literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches.
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