mlsanityData to 7 Oct 2026

LoGex: Improved tail detection of extremely rare histopathology classes via guided diffusion

arXiv 2409.01317 ↗First posted 2 Sep 2024cs.CV cs.LGcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

In realistic medical settings, the data are often inherently long-tailed, with most samples concentrated in a few classes and a long tail of rare classes, usually containing just a few samples. This distribution presents a significant challenge because rare conditions are critical to detect and difficult to classify due to limited data. In this paper, rather than attempting to classify rare classes, we aim to detect these as out-of-distribution data reliably. We leverage low-rank adaption (LoRA) and diffusion guidance to generate targeted synthetic data for the detection problem. We significantly improve the OOD detection performance on a challenging histopathological task with only ten samples per tail class without losing classification accuracy on the head classes.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 29 of its 36 references that are in the map. First posted on arXiv 2 Sep 2024.

Builds on

29 of 36 references.

Closest ancestors · 8

Same direction, newest first

  1. 2023Dual Conditioned Diffusion Models for Out-Of-Distribution Detection: Application to Fetal Ultrasound Videos1 Nov
  2. 2023Diffusion-based Data Augmentation for Nuclei Image Segmentation22 Oct
  3. 2023Dream the Impossible: Outlier Imagination with Diffusion Models23 Sep
  4. 2023Synthesising Rare Cataract Surgery Samples with Guided Diffusion Models3 Aug
  5. 2023Unsupervised 3D out-of-distribution detection with latent diffusion models7 Jul
  6. 2023DiffMIC: Dual-Guidance Diffusion Network for Medical Image Classification19 Mar
  7. 2022Diffusion Probabilistic Models beat GANs on Medical Images14 Dec
  8. 2022Denoising diffusion models for out-of-distribution detection14 Nov

Other references · 21

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  6. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  7. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  8. 2020Energy-based Out-of-distribution Detectioncited by 1,296
  9. 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,239
  10. 2019Learning Imbalanced Datasets with Label-Distribution-Aware Margin Losscited by 987
  11. 2018Deep Anomaly Detection with Outlier Exposurecited by 939
  12. 2019Decoupling Representation and Classifier for Long-Tailed Recognitioncited by 806
9 more
  1. 2019Scaling Out-of-Distribution Detection for Real-World Settingscited by 465
  2. 2023Effective Data Augmentation With Diffusion Modelscited by 282
  3. 2020A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promisescited by 230
  4. 2021A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detectioncited by 206
  5. 2023End-to-End Diffusion Latent Optimization Improves Classifier Guidancecited by 107
  6. 2023Differentially Private Diffusion Models Generate Useful Synthetic Imagescited by 85
  7. 2021Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditionscited by 68
  8. 2022Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Studycited by 45
  9. 2023Feedback-guided Data Synthesis for Imbalanced Classificationcited by 33

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