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Rethinking Polyp Segmentation From An Out-of-distribution Perspective
Unlike existing fully-supervised approaches, we rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but...
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Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation
Clinically deployed deep learning-based segmentation models are known to fail on data outside of their training distributions While clinicians review...
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Probing Contextual Diversity for Dense Out-of-Distribution Detection
Detection of out-of-distribution (OoD) samples in the context of image classification has recently become an area of interest and active study, along...
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On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness
We study the impact of different loss functions on lesion segmentation from medical images. Although the Cross-Entropy (CE) loss is the most popular...
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Redesigning Out-of-Distribution Detection on 3D Medical Images
Detecting out-of-distribution (OOD) samples for trusted medical image segmentation remains a significant challenge. The critical issue here is the...
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Using Out-of-Distribution Detection for Model Refinement in Cardiac Image Segmentation
We introduce a new learning framework that builds upon the recent progress achieved by methods for quality control (QC) of image segmentation to...
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Characterization of Out-of-distribution Samples from Uncertainty Maps Using Supervised Machine Learning
The quality of land use maps often refers to the data quality, but distributional uncertainty between training and test data must also be considered....
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Out-of-Distribution with Text-to-Image Diffusion Models
Out-of-distribution detection, identifying unexpected data from the known concepts, is essential for reliable machine learning. We present a novel...
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A Stable Vision Transformer for Out-of-Distribution Generalization
Vision Transformer (ViT) has achieved amazing results in many visual applications where training and testing instances are drawn from the independent...
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Improving Out-of-Distribution Detection with Margin-Based Prototype Learning
Deep Neural Networks often make overconfident predictions when encountering out-of-distribution (OOD) data. Previous prototype-based methods...
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Decoupled Mixup for Out-of-Distribution Visual Recognition
Convolutional neural networks (CNN) have demonstrated remarkable performance, when the training and testing data are from the same distribution....
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Generalized Out-of-Distribution Detection: A Survey
Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of machine learning systems. For instance, in autonomous...
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Gaussian-Based Approach for Out-of-Distribution Detection in Deep Learning
When dealing with Deep Learning applications for open-set problems, detecting unknown samples is crucial for ensuring the model’s robustness....
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CS-UNet: A generalizable and flexible segmentation algorithm
This study introduces a novel U-shaped image-segmentation algorithm, CS-UNet, which contains parallel CNN and Transformer encoders. This algorithm...
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Out-of-distribution- and location-aware PointNets for real-time 3D road user detection without a GPU
3D road user detection is an essential task for autonomous vehicles and mobile robots, and it plays a key role, for instance, in obstacle avoidance...
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Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects
In this work we present two video test data sets for the novel computer vision (CV) task of out of distribution tracking (OOD tracking). Here, OOD...
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MIM-OOD: Generative Masked Image Modelling for Out-of-Distribution Detection in Medical Images
Unsupervised Out-of-Distribution (OOD) detection consists in identifying anomalous regions in images leveraging only models trained on images of...
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A Multi-scale Framework for Out-of-Distribution Detection in Dermoscopic Images
The automatic detection of skin diseases via dermoscopic images can improve the efficiency in diagnosis and help doctors make more accurate...
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CellSegUNet: an improved deep segmentation model for the cell segmentation based on UNet++ and residual UNet models
Cell nucleus segmentation is an important method that is widely used in the diagnosis and treatment of many diseases, as well as counting and...
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Nonparametric K-means clustering-based adaptive unsupervised colour image segmentation
Image segmentation focuses at highlighting region of interest within the image, by accumulation of pixels based on given properties. This task...
