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Handling label Uncertainty on the Example of Automatic Detection of Shepherd's Crook RCA in Coronary CT Angiography

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arXiv 2023年

作者： Denzinger, Felix Wels, Michael Taubmann, Oliver Kordon, Florian Wagner, Fabian Mehltretter, Stephanie Gülsün, Mehmet A. Schöbinger, Max André, Florian Buß, Sebastian Görich, Johannes Sühling, Michael Maier, Andreas Breininger, Katharina Pattern Recognition Lab FAU Erlangen-Nürnberg Erlangen Germany Siemens Healthcare GmbH Computed Tomography Forchheim Germany Das Radiologische Zentrum Sinsheim-Eberbach-Erbach-Walldorf Heidelberg Germany Department Artificial Intelligence in Biomedical Engineering FAU Erlangen-Nürnberg Germany

Coronary artery disease (CAD) is often treated minimally invasively with a catheter being inserted into the diseased coronary vessel. If a patient exhibits a Shepherd’s Crook (SC) Right Coronary Artery (RCA) – an anatomical norm variant of the coronary vasculature – the complexity of this procedure is increased. Automated reporting of this variant from coronary CT angiography screening would ease prior risk assessment. We propose a 1D convolutional neural network which leverages a sequence of residual dilated convolutions to automatically determine this norm variant from a prior extracted vessel centerline. As the SC RCA is not clearly defined with respect to concrete measurements, labeling also includes qualitative aspects. Therefore, 4.23 % samples in our dataset of 519 RCA centerlines were labeled as unsure SC RCAs, with 5.97 % being labeled as sure SC RCAs. We explore measures to handle this label uncertainty, namely global/model-wise random assignment, exclusion, and soft label assignment. Furthermore, we evaluate how this uncertainty can be leveraged for the determination of a rejection class. With our best configuration, we reach an area under the receiver operating characteristic curve (AUC) of 0.938 on confident labels. Moreover, we observe an increase of up to 0.020 AUC when rejecting 10 % of the data and leveraging the labeling uncertainty information in the exclusion process. © 2023, CC BY-NC-SA.

关键词： Risk assessment

Meta Balanced Network for Fair Face recognition

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arXiv 2022年

作者： Wang, Mei Zhang, Yaobin Deng, Weihong The Pattern Recognition and Intelligent System Lab. School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Key Lab. of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

Although deep face recognition has achieved impressive progress in recent years, controversy has arisen regarding discrimination based on skin tone, questioning their deployment into real-world scenarios. In this paper, we aim to systematically and scientifically study this bias from both data and algorithm aspects. First, using the dermatologist approved Fitzpatrick Skin Type classification system and Individual Typology Angle, we contribute a benchmark called Identity Shades (IDS) database, which effectively quantifies the degree of the bias with respect to skin tone in existing face recognition algorithms and commercial APIs. Further, we provide two skin-tone aware training datasets, called BUPT-Globalface dataset and BUPT-Balancedface dataset, to remove bias in training data. Finally, to mitigate the algorithmic bias, we propose a novel meta-learning algorithm, called Meta Balanced Network (MBN), which learns adaptive margins in large margin loss such that the model optimized by this loss can perform fairly across people with different skin tones. To determine the margins, our method optimizes a meta skewness loss on a clean and unbiased meta set and utilizes backward-on-backward automatic differentiation to perform a second order gradient descent step on the current margins. Extensive experiments show that MBN successfully mitigates bias and learns more balanced performance for people with different skin tones in face recognition. The proposed datasets are available at http://***/RFW/***. © 2022, CC BY.

关键词： Face recognition

Inherently Interpretable Multi-label Classification Using Class-Specific Counterfactuals

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arXiv 2023年

作者： Sun, Susu Woerner, Stefano Maier, Andreas Koch, Lisa M. Baumgartner, Christian F. Cluster of Excellence – Machine Learning for Science University of Tübingen Germany Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Hertie Institute for Artificial Intelligence in Brain Health University of Tübingen Germany Institute of Ophthalmic Research University of Tübingen Germany

Interpretability is essential for machine learning algorithms in high-stakes application fields such as medical image analysis. However, high-performing black-box neural networks do not provide explanations for their predictions, which can lead to mistrust and suboptimal human-ML collaboration. Post-hoc explanation techniques, which are widely used in practice, have been shown to suffer from severe conceptual problems. Furthermore, as we show in this paper, current explanation techniques do not perform adequately in the multi-label scenario, in which multiple medical findings may co-occur in a single image. We propose Attri-Net1, an inherently interpretable model for multi-label classification. Attri-Net is a powerful classifier that provides transparent, trustworthy, and human-understandable explanations. The model first generates class-specific attribution maps based on counterfactuals to identify which image regions correspond to certain medical findings. Then a simple logistic regression classifier is used to make predictions based solely on these attribution maps. We compare Attri-Net to five post-hoc explanation techniques and one inherently interpretable classifier on three chest X-ray datasets. We find that Attri-Net produces high-quality multi-label explanations consistent with clinical knowledge and has comparable classification performance to state-of-the-art classification models. © 2023, CC BY.

关键词： Classification (of information)

Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentation

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arXiv 2023年

作者： Zhao, Gangming Liang, Kongming Pan, Chengwei Zhang, Fandong Wu, Xianpeng Hu, Xinyang Yu, Yizhou The Department of Computer Science The University of Hong Kong Hong Kong Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Institute of Artificial Intelligence Beihang University Beijing China The AI Lab Deepwise Healthcare Beijing China Department of Cardiology of the Second Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Key Laboratory of Cardiovascular of Zhejiang Province Hangzhou China

Vessel segmentation is widely used to help with vascular disease diagnosis. Vessels reconstructed using existing methods are often not sufficiently accurate to meet clinical use standards. This is because 3D vessel structures are highly complicated and exhibit unique characteristics, including sparsity and anisotropy. In this paper, we propose a novel hybrid deep neural network for vessel segmentation. Our network consists of two cascaded subnetworks performing initial and refined segmentation respectively. The second subnetwork further has two tightly coupled components, a traditional CNN-based U-Net and a graph U-Net. Cross-network multi-scale feature fusion is performed between these two U-shaped networks to effectively support high-quality vessel segmentation. The entire cascaded network can be trained from end to end. The graph in the second subnetwork is constructed according to a vessel probability map as well as appearance and semantic similarities in the original CT volume. To tackle the challenges caused by the sparsity and anisotropy of vessels, a higher percentage of graph nodes are distributed in areas that potentially contain vessels while a higher percentage of edges follow the orientation of potential nearby vessels. Extensive experiments demonstrate our deep network achieves state-of-the-art 3D vessel segmentation performance on multiple public and in-house datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Anisotropy

Image compression with recurrent neural network and generalized divisive normalization

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arXiv 2021年

作者： Islam, Khawar Dang, L. Minh Lee, Sujin Moon, Hyeonjoon Computer Vision and Pattern Recognition Lab. Sejong University Korea Republic of Department of Artificial Intelligence Sejong University Korea Republic of

Image compression is a method to remove spatial redundancy between adjacent pixels and reconstruct a high-quality image. In the past few years, deep learning has gained huge attention from the research community and produced promising image reconstruction results. Therefore, recent methods focused on developing deeper and more complex networks, which significantly increased network complexity. In this paper, two effective novel blocks are developed: analysis and synthesis block that employs the convolution layer and Generalized Divisive Normalization (GDN) in the variable-rate encoder and decoder side. Our network utilizes a pixel RNN approach for quantization. Furthermore, to improve the whole network, we encode a residual image using LSTM cells to reduce unnecessary information. Experimental results demonstrated that the proposed variable-rate framework with novel blocks outperforms existing methods and standard image codecs, such as George’s [11] and JPEG in terms of image similarity. © 2021, CC BY.

关键词： Image compression

Progressive co-attention network for fine-grained visual classification

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arXiv 2021年

作者： Zhang, Tian Chang, Dongliang Ma, Zhanyu Guo, Jun Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications Beijing China Beijing Academy of Artificial Intelligence Beijing China

Fine-grained visual classification aims to recognize images belonging to multiple sub-categories within a same category. It is a challenging task due to the inherently subtle variations among highly-confused categories. Most existing methods only take an individual image as input, which may limit the ability of models to recognize contrastive clues from different images. In this paper, we propose an effective method called progressive co-attention network (PCA-Net) to tackle this problem. Specifically, we calculate the channel-wise similarity by encouraging interaction between the feature channels within same-category image pairs to capture the common discriminative features. Considering that complementary information is also crucial for recognition, we erase the prominent areas enhanced by the channel interaction to force the network to focus on other discriminative regions. The proposed model has achieved competitive results on three fine-grained visual classification benchmark datasets: CUB-200-2011, Stanford Cars, and FGVC Aircraft. © 2021, CC BY.

关键词： Classification (of information)

Leveraging Image Captions for Selective Whole Slide Image Annotation

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arXiv 2024年

作者： Qiu, Jingna Aubreville, Marc Wilm, Frauke Öttl, Mathias Utz, Jonas Schlereth, Maja Breininger, Katharina Department Artificial Intelligence in Biomedical Engineering FAU Erlangen-Nürnberg Erlangen Germany Technische Hochschule Ingolstadt Ingolstadt Germany Flensburg University of Applied Sciences Flensburg Germany Pattern Recognition Lab Department of Computer Science FAU Erlangen-Nürnberg Erlangen Germany Center for AI and Data Science Universität Würzburg Würzburg Germany

Acquiring annotations for whole slide images (WSIs)-based deep learning tasks, such as creating tissue segmentation masks or detecting mitotic figures, is a laborious process due to the extensive image size and the significant manual work involved in the annotation. This paper focuses on identifying and annotating specific image regions that optimize model training, given a limited annotation budget. While random sampling helps capture data variance by collecting annotation regions throughout the WSI, insufficient data curation may result in an inadequate representation of minority classes. Recent studies proposed diversity sampling to select a set of regions that maximally represent unique characteristics of the WSIs. This is done by pretraining on unlabeled data through self-supervised learning and then clustering all regions in the latent space. However, establishing the optimal number of clusters can be difficult and not all clusters are task-relevant. This paper presents prototype sampling, a new method for annotation region selection. It discovers regions exhibiting typical characteristics of each task-specific class. The process entails recognizing class prototypes from extensive histopathology image-caption databases and detecting unlabeled image regions that resemble these prototypes. Our results show that prototype sampling is more effective than random and diversity sampling in identifying annotation regions with valuable training information, resulting in improved model performance in semantic segmentation and mitotic figure detection tasks. Code is available at https://***/DeepMicroscopy/Prototype-sampling. Copyright © 2024, The Authors. All rights reserved.

关键词： Image annotation

Initial Investigations Towards Non-invasive Monitoring of Chronic Wound Healing Using Deep Learning and Ultrasound Imaging

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Initial Investigations Towards Non-invasive Monitoring of Ch...

German Workshop on Medical Image Computing, 2022

作者： Schlereth, Maja Stromer, Daniel Mantri, Yash Tsujimoto, Jason Breininger, Katharina Maier, Andreas Anderson, Caesar Garimella, Pranav S. Jokerst, Jesse V. Department Artificial Intelligence in Biomedical Engineering FAU Erlangen-Nürnberg Erlangen Germany Pattern Recognition Lab FAU Erlangen-Nürnberg Erlangen Germany Department of Bioengineering University of California San Diego United States Department of Emergency Medicine San Diego United States Division of Nephrology and Hypertension Department of Medicine San Diego United States Department of Nanoengineering University of California San Diego United States

Progressive Co-Attention Network for Fine-Grained Visual Classification

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Progressive Co-Attention Network for Fine-Grained Visual Cla...

IEEE Visual Communications and Image Processing (VCIP)

作者： Tian Zhang Dongliang Chang Zhanyu Ma Jun Guo Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China Beijing Academy of Artificial Intelligence Beijing China

ISBN: (纸本)9781728173221

关键词： Visualization Image recognition Visual communication Atmospheric modeling Force Benchmark testing Automobiles