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DualAttNet: Synergistic Fusion of Image-level and Fine-Grained Disease Attention for Multi-Label Lesion Detection in Chest X-rays (2306.13813v1)

Published 23 Jun 2023 in eess.IV and cs.CV

Abstract: Chest radiographs are the most commonly performed radiological examinations for lesion detection. Recent advances in deep learning have led to encouraging results in various thoracic disease detection tasks. Particularly, the architecture with feature pyramid network performs the ability to recognise targets with different sizes. However, such networks are difficult to focus on lesion regions in chest X-rays due to their high resemblance in vision. In this paper, we propose a dual attention supervised module for multi-label lesion detection in chest radiographs, named DualAttNet. It efficiently fuses global and local lesion classification information based on an image-level attention block and a fine-grained disease attention algorithm. A binary cross entropy loss function is used to calculate the difference between the attention map and ground truth at image level. The generated gradient flow is leveraged to refine pyramid representations and highlight lesion-related features. We evaluate the proposed model on VinDr-CXR, ChestX-ray8 and COVID-19 datasets. The experimental results show that DualAttNet surpasses baselines by 0.6% to 2.7% mAP and 1.4% to 4.7% AP50 with different detection architectures. The code for our work and more technical details can be found at https://github.com/xq141839/DualAttNet.

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References (45)
  1. Interpretation of plain chest roentgenogram, Chest 141 (2012) 545–558.
  2. Diagnostic accuracy of mediastinal width measurement on posteroanterior and anteroposterior chest radiographs in the depiction of acute nontraumatic thoracic aortic dissection, Emergency radiology 19 (2012) 309–315.
  3. Automatic rapid segmentation of human lung from 2d chest x-ray images, in: Proc. of MICCAI workshop on Sparsity Techniques in Medical Imaging, Citeseer, 2012.
  4. Automated classification of radiology reports for acute lung injury: comparison of keyword and machine learning based natural language processing approaches, in: 2009 IEEE international conference on bioinformatics and biomedicine workshop, IEEE, 2009, pp. 314–319.
  5. An automatic computer-aided detection scheme for pneumoconiosis on digital chest radiographs, Journal of digital imaging 24 (2011) 382–393.
  6. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning, arXiv preprint arXiv:1711.05225 (2017).
  7. R. Girshick, Fast r-cnn, in: Proceedings of the IEEE international conference on computer vision, 2015, pp. 1440–1448.
  8. Yolov4: Optimal speed and accuracy of object detection, arXiv preprint arXiv:2004.10934 (2020).
  9. Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
  10. Densely connected convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700–4708.
  11. Feature pyramid networks for object detection, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2117–2125.
  12. Vindr-cxr: An open dataset of chest x-rays with radiologist’s annotations, Scientific Data 9 (2022) 429.
  13. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2097–2106.
  14. Covid-19 image data collection, arXiv preprint arXiv:2003.11597 (2020).
  15. Object detection in 20 years: A survey, Proceedings of the IEEE (2023).
  16. Focal loss for dense object detection, in: Proceedings of the IEEE international conference on computer vision, 2017, pp. 2980–2988.
  17. You only look once: Unified, real-time object detection, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788.
  18. You only look one-level feature, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 13039–13048.
  19. Yolox: Exceeding yolo series in 2021, arXiv preprint arXiv:2107.08430 (2021).
  20. Yolov6: A single-stage object detection framework for industrial applications, arXiv preprint arXiv:2209.02976 (2022).
  21. Varifocalnet: An iou-aware dense object detector, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 8514–8523.
  22. Squeeze-and-excitation networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7132–7141.
  23. Global second-order pooling convolutional networks, in: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, 2019, pp. 3024–3033.
  24. Eca-net: Efficient channel attention for deep convolutional neural networks, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 11534–11542.
  25. Fcanet: Frequency channel attention networks, in: Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 783–792.
  26. An image is worth 16x16 words: Transformers for image recognition at scale, arXiv preprint arXiv:2010.11929 (2020).
  27. Tokens-to-token vit: Training vision transformers from scratch on imagenet, in: Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 558–567.
  28. Swin transformer: Hierarchical vision transformer using shifted windows, in: Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 10012–10022.
  29. Masked autoencoders are scalable vision learners, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16000–16009.
  30. Cbam: Convolutional block attention module, in: Proceedings of the European conference on computer vision (ECCV), 2018, pp. 3–19.
  31. Single-shot refinement neural network for object detection, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4203–4212.
  32. J. Redmon, A. Farhadi, Yolov3: An incremental improvement, arXiv preprint arXiv:1804.02767 (2018).
  33. Medical image analysis using convolutional neural networks: a review, Journal of medical systems 42 (2018) 1–13.
  34. Epsanet: An efficient pyramid squeeze attention block on convolutional neural network, in: Proceedings of the Asian Conference on Computer Vision, 2022, pp. 1161–1177.
  35. Ccnet: Criss-cross attention for semantic segmentation, in: Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 603–612.
  36. On the integration of self-attention and convolution, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 815–825.
  37. Contextual transformer networks for visual recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence (2022).
  38. Non-deep networks, Advances in Neural Information Processing Systems 35 (2022) 6789–6801.
  39. ultralytics/yolov5: V7. 0-yolov5 sota realtime instance segmentation, Zenodo (2022).
  40. Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 7464–7475.
  41. Efficientdet: Scalable and efficient object detection, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 10781–10790.
  42. Operating characteristics, signal detectability, and the method of free response, The Journal of the Acoustical Society of America 33 (1961) 993–1007.
  43. A survey on incorporating domain knowledge into deep learning for medical image analysis, Medical Image Analysis 69 (2021) 101985.
  44. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer, Jama 318 (2017) 2199–2210.
  45. M. B. Muhammad, M. Yeasin, Eigen-cam: Class activation map using principal components, in: 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 2020, pp. 1–7.
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