TriAug: Out-of-Distribution Detection for Imbalanced Breast Lesion in Ultrasound

• URL: http://arxiv.org/abs/2402.07452v2
• Date: Tue, 27 Feb 2024 02:19:54 GMT
• Title: TriAug: Out-of-Distribution Detection for Imbalanced Breast Lesion in Ultrasound
• Authors: Yinyu Ye, Shijing Chen, Dong Ni, Ruobing Huang
• Abstract summary: We propose a novel framework built upon a long-tailed OOD detection task for breast ultrasound images. It is equipped with a triplet state augmentation which improves ID classification accuracy while maintaining a promising OOD detection performance.
• Score: 6.3267889365863414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Different diseases, such as histological subtypes of breast lesions, have severely varying incidence rates. Even trained with substantial amount of in-distribution (ID) data, models often encounter out-of-distribution (OOD) samples belonging to unseen classes in clinical reality. To address this, we propose a novel framework built upon a long-tailed OOD detection task for breast ultrasound images. It is equipped with a triplet state augmentation (TriAug) which improves ID classification accuracy while maintaining a promising OOD detection performance. Meanwhile, we designed a balanced sphere loss to handle the class imbalanced problem. Experimental results show that the model outperforms state-of-art OOD approaches both in ID classification (F1-score=42.12%) and OOD detection (AUROC=78.06%).

Related papers

• Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution [38.844580833635725]
  We present a training-time regularization technique to mitigate the bias and boost imbalanced OOD detectors across architecture designs. Our method translates into consistent improvements on the representative CIFAR10-LT, CIFAR100-LT, and ImageNet-LT benchmarks.
  arXiv  Detail & Related papers  (2024-07-23T12:28:59Z)
• 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)
• 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)
• Model-free Test Time Adaptation for Out-Of-Distribution Detection [62.49795078366206]
  We propose a Non-Parametric Test Time textbfAdaptation framework for textbfDistribution textbfDetection (abbr) abbr utilizes online test samples for model adaptation during testing, enhancing adaptability to changing data distributions. We demonstrate the effectiveness of abbr through comprehensive experiments on multiple OOD detection benchmarks.
  arXiv  Detail & Related papers  (2023-11-28T02:00:47Z)
• Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos [9.194340639317401]
  Out-of-distribution (OOD) detection is essential to improve the reliability of machine learning models. We introduce dual-conditioned diffusion models (DCDM) where we condition the model on in-distribution class information. This constrains the generative manifold of the model to generate images structurally and semantically similar to those within the in-distribution.
  arXiv  Detail & Related papers  (2023-11-01T12:10:55Z)
• Redesigning Out-of-Distribution Detection on 3D Medical Images [0.5207048071888255]
  We redesign the OOD detection problem according to the specifics of volumetric medical imaging and related downstream tasks. We propose using the downstream model's performance as a pseudometric between images to define abnormal samples. We incorporate this weighting in a new metric called Expected Performance Drop (EPD)
  arXiv  Detail & Related papers  (2023-08-07T10:28:31Z)
• 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)
• A knee cannot have lung disease: out-of-distribution detection with in-distribution voting using the medical example of chest X-ray classification [58.720142291102135]
  The study employed the commonly used chest X-ray classification model, CheXnet, trained on the chest X-ray 14 data set. To detect OOD data for multi-label classification, we proposed in-distribution voting (IDV) The proposed IDV approach trained on ID (chest X-ray 14) and OOD data (IRMA and ImageNet) achieved, on average, 0.999 OOD detection AUC across the three data sets.
  arXiv  Detail & Related papers  (2022-08-01T18:20:36Z)
• CVAD: A generic medical anomaly detector based on Cascade VAE [2.647674705784439]
  We focus on the generalizability of OOD detection for medical images and propose a self-supervised Cascade Variational autoencoder-based Anomaly Detector (CVAD) We use a variational autoencoders' cascade architecture, which combines latent representation at multiple scales, before being fed to a discriminator to distinguish the OOD data from the in-distribution (ID) data. We compare the performance with the state-of-the-art deep learning models to demonstrate our model's efficacy on various open-access medical imaging datasets for both intra- and inter-class OOD.
  arXiv  Detail & Related papers  (2021-10-29T14:20:43Z)
• 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.