Generalized Out-of-Distribution Detection: A Survey

Yang, Jingkang; Zhou, Kaiyang; Li, Yixuan; Liu, Ziwei

doi:10.48550/arxiv.2110.11334

Cited by 125 publications

(178 citation statements)

References 112 publications

Supporting

Mentioning

131

Contrasting

Order By: Relevance

“…In medical imaging applications, these distribution shifts in data arise due to several factors that include: images acquired with a different device model at a different hospital, images of some unseen disease not in the training dataset, images that are incorrectly prepared, e.g., poor contrast, blurry images, etc. Extensive research exists on CNN-based out-of-distribution detection approaches in medical imaging [431]- [435]. Recently, few attempts have been made to show that large-scale pretrained ViTs, due to their high-quality representations, can significantly improve the state-of-the-art on a range of out-ofdistribution tasks across different data modalities [386], [430], [436].…”

Section: Domain Adaptation and Out-of-distribution Detectionmentioning

confidence: 99%

Transformers in Medical Imaging: A Survey

Shamshad¹,

Khan²,

Zamir³

et al. 2022

Preprint

View full text Add to dashboard Cite

Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as de facto operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues. Specifically, we survey the use of Transformers in medical image segmentation, detection, classification, reconstruction, synthesis, registration, clinical report generation, and other tasks. In particular, for each of these applications, we develop taxonomy, identify application-specific challenges as well as provide insights to solve them, and highlight recent trends. Further, we provide a critical discussion of the field's current state as a whole, including the identification of key challenges, open problems, and outlining promising future directions. We hope this survey will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of Transformer models in medical imaging. Finally, to cope with the rapid development in this field, we intend to regularly update the relevant latest papers and their open-source implementations at https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging.

show abstract

Section: Domain Adaptation and Out-of-distribution Detectionmentioning

confidence: 99%

Transformers in Medical Imaging: A Survey

Shamshad¹,

Khan²,

Zamir³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Some researchers have also studied learning the novel objects after they are detected (Bendale and Boult 2015;Fei, Wang, and Liu 2016;Xu et al 2019) and manually labeled. A survey of the topic can be found in (Yang et al 2021). A position paper presented some nice blue sky ideas about open world learning in (Langley 2020), but it does not have sufficient details or an implemented system.…”

Section: Comparison With Related Workmentioning

confidence: 99%

AI Autonomy: Self-Initiation, Adaptation and Continual Learning

Liu¹,

Mazumder²,

Robertson³

et al. 2022

Preprint

View full text Add to dashboard Cite

As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can (1) learn by themselves continually in a selfmotivated and self-initiated manner rather than being retrained offline periodically on the initiation of human engineers and (2) accommodate or adapt to unexpected or novel circumstances. As the real-world is an open environment that is full of unknowns or novelties, detecting novelties, characterizing them, accommodating or adapting to them, and gathering ground-truth training data and incrementally learning the unknowns/novelties are critical to making the AI agent more and more knowledgeable and powerful over time. The key challenge is how to automate the process so that it is carried out continually on the agent's own initiative and through its own interactions with humans, other agents and the environment just like human on-the-job learning. This paper proposes a framework (called SOLA) for this learning paradigm to promote the research of building autonomous and continual learning enabled AI agents. To show feasibility, an implemented agent is also described.

show abstract

“…The latter property is especially important for the adoption of OOD detection methods in real-world production environments, where the overhead cost of retraining can be sometimes prohibitive. A comprehensive survey on OOD detection can be found in [53].…”

Section: Related Workmentioning

confidence: 99%

DICE: Leveraging Sparsification for Out-of-Distribution Detection

Sun¹,

Li²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world. Previous methods commonly rely on an OOD score derived from the overparameterized weight space, while largely overlooking the role of sparsification. In this paper, we reveal important insights that reliance on unimportant weights and units can directly attribute to the brittleness of OOD detection. To mitigate the issue, we propose a sparsification-based OOD detection framework termed DICE. Our key idea is to rank weights based on a measure of contribution, and selectively use the most salient weights to derive the output for OOD detection. We provide both empirical and theoretical insights, characterizing and explaining the mechanism by which DICE improves OOD detection. By pruning away noisy signals, DICE provably reduces the output variance for OOD data, resulting in a sharper output distribution and stronger separability from ID data. DICE establishes superior performance, reducing the FPR95 by up to 24.69% compared to the previous best method 1 .

show abstract

Generalized Out-of-Distribution Detection: A Survey

Cited by 125 publications

References 112 publications

Transformers in Medical Imaging: A Survey

Transformers in Medical Imaging: A Survey

AI Autonomy: Self-Initiation, Adaptation and Continual Learning

DICE: Leveraging Sparsification for Out-of-Distribution Detection

Contact Info

Product

Resources

About