Improving Out-of-Distribution Detection in Echocardiographic View Classication through Enhancing Semantic Features

• URL: http://arxiv.org/abs/2308.16483v2
• Date: Fri, 24 Nov 2023 03:07:39 GMT
• Title: Improving Out-of-Distribution Detection in Echocardiographic View Classication through Enhancing Semantic Features
• Authors: Jaeik Jeon, Seongmin Ha, Yeonggul Jang, Yeonyee E. Yoon, Jiyeon Kim, Hyunseok Jeong, Dawun Jeong, Youngtaek Hong, Seung-Ah Lee Hyuk-Jae Chang
• Abstract summary: We introduce a novel use of label smoothing to enhance semantic feature representation in echocardiographic images. By combining label smoothing with MD-based OOD detection, we establish a new benchmark for accuracy in echocardiographic OOD detection.
• Score: 1.9892308483583199
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In echocardiographic view classification, accurately detecting out-of-distribution (OOD) data is essential but challenging, especially given the subtle differences between in-distribution and OOD data. While conventional OOD detection methods, such as Mahalanobis distance (MD) are effective in far-OOD scenarios with clear distinctions between distributions, they struggle to discern the less obvious variations characteristic of echocardiographic data. In this study, we introduce a novel use of label smoothing to enhance semantic feature representation in echocardiographic images, demonstrating that these enriched semantic features are key for significantly improving near-OOD instance detection. By combining label smoothing with MD-based OOD detection, we establish a new benchmark for accuracy in echocardiographic OOD detection.

Related papers

• Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution Detection [24.557227100200215]
  Out-of-distribution (OOD) detection is crucial for deploying reliable machine learning models in open-world applications. Recent advances in CLIP-based OOD detection have shown promising results via regularizing prompt tuning with OOD features extracted from ID data. We propose a novel framework, namely, Self-Calibrated Tuning (SCT), to mitigate this problem for effective OOD detection with only the given few-shot ID data.
  arXiv  Detail & Related papers  (2024-11-05T02:29:16Z)
• Margin-bounded Confidence Scores for Out-of-Distribution Detection [2.373572816573706]
  We propose a novel method called Margin bounded Confidence Scores (MaCS) to address the nontrivial OOD detection problem. MaCS enlarges the disparity between ID and OOD scores, which in turn makes the decision boundary more compact. Experiments on various benchmark datasets for image classification tasks demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2024-09-22T05:40:25Z)
• TTA-OOD: Test-time Augmentation for Improving Out-of-Distribution Detection in Gastrointestinal Vision [6.290783164114315]
  We introduce a test-time augmentation segment into the OOD detection pipeline. This augmentation shifts the pixel space, which translates into a more distinct semantic representation for OOD examples. We evaluate our method against existing state-of-the-art OOD scores.
  arXiv  Detail & Related papers  (2024-07-19T04:50:54Z)
• Rethinking the Evaluation of Out-of-Distribution Detection: A Sorites Paradox [70.57120710151105]
  Most existing out-of-distribution (OOD) detection benchmarks classify samples with novel labels as the OOD data. Some marginal OOD samples actually have close semantic contents to the in-distribution (ID) sample, which makes determining the OOD sample a Sorites Paradox. We construct a benchmark named Incremental Shift OOD (IS-OOD) to address the issue.
  arXiv  Detail & Related papers  (2024-06-14T09:27:56Z)
• Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models [71.39421638547164]
  We propose to detect OOD molecules by adopting an auxiliary diffusion model-based framework, which compares similarities between input molecules and reconstructed graphs. Due to the generative bias towards reconstructing ID training samples, the similarity scores of OOD molecules will be much lower to facilitate detection. Our research pioneers an approach of Prototypical Graph Reconstruction for Molecular OOD Detection, dubbed as PGR-MOOD and hinges on three innovations.
  arXiv  Detail & Related papers  (2024-04-24T03:25:53Z)
• EAT: Towards Long-Tailed Out-of-Distribution Detection [55.380390767978554]
  This paper addresses the challenging task of long-tailed OOD detection. The main difficulty lies in distinguishing OOD data from samples belonging to the tail classes. We propose two simple ideas: (1) Expanding the in-distribution class space by introducing multiple abstention classes, and (2) Augmenting the context-limited tail classes by overlaying images onto the context-rich OOD data.
  arXiv  Detail & Related papers  (2023-12-14T13:47:13Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need [52.88953913542445]
  We find surprisingly that simply using reconstruction-based methods could boost the performance of OOD detection significantly. We take Masked Image Modeling as a pretext task for our OOD detection framework (MOOD)
  arXiv  Detail & Related papers  (2023-02-06T08:24:41Z)
• On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution Detection [7.221206118679026]
  The ability to detect Out-of-Distribution (OOD) data is important in safety-critical applications of deep learning. An existing intuition in the literature is that the diversity of Deep Ensemble predictions indicates distributional shift. We show experimentally that this intuition is not valid on ImageNet-scale OOD detection.
  arXiv  Detail & Related papers  (2022-07-15T15:02:38Z)
• On the Impact of Spurious Correlation for Out-of-distribution Detection [14.186776881154127]
  We present a new formalization and model the data shifts by taking into account both the invariant and environmental features. Our results suggest that the detection performance is severely worsened when the correlation between spurious features and labels is increased in the training set.
  arXiv  Detail & Related papers  (2021-09-12T23:58:17Z)
• Robust Out-of-distribution Detection for Neural Networks [51.19164318924997]
  We show that existing detection mechanisms can be extremely brittle when evaluating on in-distribution and OOD inputs. We propose an effective algorithm called ALOE, which performs robust training by exposing the model to both adversarially crafted inlier and outlier examples.
  arXiv  Detail & Related papers  (2020-03-21T17:46:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.