mlsanityData to 7 Oct 2026

CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound

arXiv 2206.08524 ↗First posted 17 Jun 2022cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Precise and rapid categorization of images in the B-scan ultrasound modality is vital for diagnosing ocular diseases. Nevertheless, distinguishing various diseases in ultrasound still challenges experienced ophthalmologists. Thus a novel contrastive disentangled network (CDNet) is developed in this work, aiming to tackle the fine-grained image categorization (FGIC) challenges of ocular abnormalities in ultrasound images, including intraocular tumor (IOT), retinal detachment (RD), posterior scleral staphyloma (PSS), and vitreous hemorrhage (VH). Three essential components of CDNet are the weakly-supervised lesion localization module (WSLL), contrastive multi-zoom (CMZ) strategy, and hyperspherical contrastive disentangled loss (HCD-Loss), respectively. These components facilitate feature disentanglement for fine-grained recognition in both the input and output aspects. The proposed CDNet is validated on our ZJU Ocular Ultrasound Dataset (ZJUOUSD), consisting of 5213 samples. Furthermore, the generalization ability of CDNet is validated on two public and widely-used chest X-ray FGIC benchmarks. Quantitative and qualitative results demonstrate the efficacy of our proposed CDNet, which achieves state-of-the-art performance in the FGIC task. Code is available at: https://github.com/ZeroOneGame/CDNet-for-OUS-FGIC .

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 33 of its 59 references that are in the map. First posted on arXiv 17 Jun 2022.

Builds on

33 of 59 references.

References · 33

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  4. 2016Densely Connected Convolutional Networkscited by 6,691
  5. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  6. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  7. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  8. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
  9. 2020Supervised Contrastive Learningcited by 2,803
  10. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,818
  11. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,054
  12. 2021CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classificationcited by 806
21 more
  1. 2018CCNet: Criss-Cross Attention for Semantic Segmentationcited by 717
  2. 2018Albumentations: fast and flexible image augmentationscited by 558
  3. 2020Gradient Starvation: A Learning Proclivity in Neural Networkscited by 262
  4. 2019Cross Attention Network for Few-shot Classificationcited by 218
  5. 2020Can AI help in screening Viral and COVID-19 pneumonia?cited by 200
  6. 2020Disentangling Label Distribution for Long-tailed Visual Recognitioncited by 172
  7. 2019Seeing What a GAN Cannot Generatecited by 148
  8. 2021When Does Contrastive Visual Representation Learning Work?cited by 92
  9. 2022Crafting Better Contrastive Views for Siamese Representation Learningcited by 67
  10. 2021Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contextscited by 59
  11. 2021Semi-supervised Contrastive Learning for Label-efficient Medical Image Segmentationcited by 52
  12. 2020Weakly Supervised Lesion Localization With Probabilistic-CAM Poolingcited by 35
  13. 2020Learning the Redundancy-free Features for Generalized Zero-Shot Object Recognitioncited by 34
  14. 2017Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detectioncited by 28
  15. 2020Variational Feature Disentangling for Fine-Grained Few-Shot Classificationcited by 26
  16. 2018Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Imagescited by 11
  17. 2021AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapycited by 10
  18. 2021Contrastive Learning for View Classification of Echocardiogramscited by 7
  19. 2021Studying the Effects of Self-Attention for Medical Image Analysiscited by 6
  20. 2020Two-Stream Compare and Contrast Network for Vertebral Compression Fracture Diagnosiscited by 3
  21. 2020Knowledge Transfer Based Fine-grained Visual Classificationcited by 1

Built on by

Not cited yet.