11 research outputs found

    ICICLE: Interpretable Class Incremental Continual Learning

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    Continual learning enables incremental learning of new tasks without forgetting those previously learned, resulting in positive knowledge transfer that can enhance performance on both new and old tasks. However, continual learning poses new challenges for interpretability, as the rationale behind model predictions may change over time, leading to interpretability concept drift. We address this problem by proposing Interpretable Class-InCremental LEarning (ICICLE), an exemplar-free approach that adopts a prototypical part-based approach. It consists of three crucial novelties: interpretability regularization that distills previously learned concepts while preserving user-friendly positive reasoning; proximity-based prototype initialization strategy dedicated to the fine-grained setting; and task-recency bias compensation devoted to prototypical parts. Our experimental results demonstrate that ICICLE reduces the interpretability concept drift and outperforms the existing exemplar-free methods of common class-incremental learning when applied to concept-based models. We make the code available.Comment: Under review, code will be shared after the acceptanc

    Lifelong Learning in the Clinical Open World

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    Despite mounting evidence that data drift causes deep learning models to deteriorate over time, the majority of medical imaging research is developed for - and evaluated on - static close-world environments. There have been exciting advances in the automatic detection and segmentation of diagnostically-relevant findings. Yet the few studies that attempt to validate their performance in actual clinics are met with disappointing results and little utility as perceived by healthcare professionals. This is largely due to the many factors that introduce shifts in medical image data distribution, from changes in the acquisition practices to naturally occurring variations in the patient population and disease manifestation. If we truly wish to leverage deep learning technologies to alleviate the workload of clinicians and drive forward the democratization of health care, we must move away from close-world assumptions and start designing systems for the dynamic open world. This entails, first, the establishment of reliable quality assurance mechanisms with methods from the fields of uncertainty estimation, out-of-distribution detection, and domain-aware prediction appraisal. Part I of the thesis summarizes my contributions to this area. I first propose two approaches that identify outliers by monitoring a self-supervised objective or by quantifying the distance to training samples in a low-dimensional latent space. I then explore how to maximize the diversity among members of a deep ensemble for improved calibration and robustness; and present a lightweight method to detect low-quality lung lesion segmentation masks using domain knowledge. Of course, detecting failures is only the first step. We ideally want to train models that are reliable in the open world for a large portion of the data. Out-of-distribution generalization and domain adaptation may increase robustness, but only to a certain extent. As time goes on, models can only maintain acceptable performance if they continue learning with newly acquired cases that reflect changes in the data distribution. The goal of continual learning is to adapt to changes in the environment without forgetting previous knowledge. One practical strategy to approach this is expansion, whereby multiple parametrizations of the model are trained and the most appropriate one is selected during inference. In the second part of the thesis, I present two expansion-based methods that do not rely on information regarding when or how the data distribution changes. Even when appropriate mechanisms are in place to fail safely and accumulate knowledge over time, this will only translate to clinical usage insofar as the regulatory framework allows it. Current regulations in the USA and European Union only authorize locked systems that do not learn post-deployment. Fortunately, regulatory bodies are noting the need for a modern lifecycle regulatory approach. I review these efforts, along with other practical aspects of developing systems that learn through their lifecycle, in the third part of the thesis. We are finally at a stage where healthcare professionals and regulators are embracing deep learning. The number of commercially available diagnostic radiology systems is also quickly rising. This opens up our chance - and responsibility - to show that these systems can be safe and effective throughout their lifespan

    List of 121 papers citing one or more skin lesion image datasets

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    Leveraging deep neural networks for automatic and standardised wound image acquisition

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    Wound monitoring is a time-consuming and error-prone activity performed daily by healthcare professionals. Capturing wound images is crucial in the current clinical practice, though image inadequacy can undermine further assessments. To provide sufficient information for wound analysis, the images should also contain a minimal periwound area. This work proposes an automatic wound image acquisition methodology that exploits deep learning models to guarantee compliance with the mentioned adequacy requirements, using a marker as a metric reference. A RetinaNet model detects the wound and marker regions, further analysed by a post-processing module that validates if both structures are present and verifies that a periwound radius of 4 centimetres is included. This pipeline was integrated into a mobile application that processes the camera frames and automatically acquires the image once the adequacy requirements are met. The detection model achieved [email protected] values of 0.39 and 0.95 for wound and marker detection, exhibiting a robust detection performance for varying acquisition conditions. Mobile tests demonstrated that the application is responsive, requiring 1.4 seconds on average to acquire an image. The robustness of this solution for real-time smartphone-based usage evidences its capability to standardise the acquisition of adequate wound images, providing a powerful tool for healthcare professionals.info:eu-repo/semantics/publishedVersio

    Segmentation of fetal 2D images with deep learning: a review

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    Image segmentation plays a vital role in providing sustainable medical care in this evolving biomedical image processing technology. Nowadays, it is considered one of the most important research directions in the computer vision field. Since the last decade, deep learning-based medical image processing has become a research hotspot due to its exceptional performance. In this paper, we present a review of different deep learning techniques used to segment fetal 2D images. First, we explain the basic ideas of each approach and then thoroughly investigate the methods used for the segmentation of fetal images. Secondly, the results and accuracy of different approaches are also discussed. The dataset details used for assessing the performance of the respective method are also documented. Based on the review studies, the challenges and future work are also pointed out at the end. As a result, it is shown that deep learning techniques are very effective in the segmentation of fetal 2D images.info:eu-repo/semantics/publishedVersio

    Polyp Segmentation with Fully Convolutional Deep Neural Networks—Extended Evaluation Study

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    Analysis of colonoscopy images plays a significant role in early detection of colorectal cancer. Automated tissue segmentation can be useful for two of the most relevant clinical target applications—lesion detection and classification, thereby providing important means to make both processes more accurate and robust. To automate video colonoscopy analysis, computer vision and machine learning methods have been utilized and shown to enhance polyp detectability and segmentation objectivity. This paper describes a polyp segmentation algorithm, developed based on fully convolutional network models, that was originally developed for the Endoscopic Vision Gastrointestinal Image Analysis (GIANA) polyp segmentation challenges. The key contribution of the paper is an extended evaluation of the proposed architecture, by comparing it against established image segmentation benchmarks utilizing several metrics with cross-validation on the GIANA training dataset. Different experiments are described, including examination of various network configurations, values of design parameters, data augmentation approaches, and polyp characteristics. The reported results demonstrate the significance of the data augmentation, and careful selection of the method’s design parameters. The proposed method delivers state-of-the-art results with near real-time performance. The described solution was instrumental in securing the top spot for the polyp segmentation sub-challenge at the 2017 GIANA challenge and second place for the standard image resolution segmentation task at the 2018 GIANA challenge
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