Abstract
Automated detection of Gallbladder Cancer (GBC) from Ultrasound (US) images is an important problem, which has drawn increased interest from researchers. However, most of these works use difficult-to-acquire information such as bounding box annotations or additional US videos. In this paper, we focus on GBC detection using only image-level labels. Such annotation is usually available based on the diagnostic report of a patient, and do not require additional annotation effort from the physicians. However, our analysis reveals that it is difficult to train a standard image classification model for GBC detection. This is due to the low inter-class variance (a malignant region usually occupies only a small portion of a US image), high intra-class variance (due to the US sensor capturing a 2D slice of a 3D object leading to large viewpoint variations), and low training data availability. We posit that even when we have only the image level label, still formulating the problem as object detection (with bounding box output) helps a deep neural network (DNN) model focus on the relevant region of interest. Since no bounding box annotations is available for training, we pose the problem as weakly supervised object detection (WSOD). Motivated by the recent success of transformer models in object detection, we train one such model, DETR, using multi-instance-learning (MIL) with self-supervised instance selection to suit the WSOD task. Our proposed method demonstrates an improvement of AP and detection sensitivity over the SOTA transformer-based and CNN-based WSOD methods. Project page is at https://gbc-iitd.github.io/wsod-gbc.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ali, S., et al.: A multi-centre polyp detection and segmentation dataset for generalisability assessment. Scientific Data 10(1), 75 (2023)
Bai, H., Zhang, R., Wang, J., Wan, X.: Weakly supervised object localization via transformer with implicit spatial calibration. In: ECCV. pp. 612–628. Springer (2022). https://doi.org/10.1007/978-3-031-20077-9_36
Basu, S., Gupta, M., Rana, P., Gupta, P., Arora, C.: Surpassing the human accuracy: Detecting gallbladder cancer from USG images with curriculum learning. In: CVPR, pp. 20886–20896 (2022)
Basu, S., Gupta, M., Rana, P., Gupta, P., Arora, C.: Radformer: transformers with global-local attention for interpretable and accurate gallbladder cancer detection. Med. Image Anal. 83, 102676 (2023)
Basu, S., Singla, S., Gupta, M., Rana, P., Gupta, P., Arora, C.: Unsupervised contrastive learning of image representations from ultrasound videos with hard negative mining. In: MICCAI, pp. 423–433. Springer (2022). https://doi.org/10.1007/978-3-031-16440-8_41
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 213–229. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13
Chen, Y., et al.: USCL: pretraining deep ultrasound image diagnosis model through video contrastive representation learning. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12908, pp. 627–637. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87237-3_60
Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89(1–2), 31–71 (1997)
Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Gao, W., et al.: Ts-cam: Token semantic coupled attention map for weakly supervised object localization. In: ICCV, pp. 2886–2895 (2021)
Gupta, P.: Imaging-based algorithmic approach to gallbladder wall thickening. World J. Gastroenterol. 26(40), 6163 (2020)
Gupta, P., et al.: Locally advanced gallbladder cancer: a review of the criteria and role of imaging. Abdominal Radiol. 46(3), 998–1007 (2021)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)
Hong, E.K., et al.: Surgical outcome and prognostic factors in patients with gallbladder carcinoma. Ann. Hepato-Biliary-Pancreat. Surg. 18(4), 129–137 (2014)
Howlader, N., et al.: Seer cancer statistics review, 1975–2014, national cancer institute, pp. 1–12. Bethesda, MD pp (2017)
Jha, D., et al.: Real-time polyp detection, localization and segmentation in colonoscopy using deep learning. IEEE Access 9, 40496–40510 (2021)
Jha, D., et al.: Kvasir-SEG: a segmented polyp dataset. In: Ro, Y.M., et al. (eds.) MMM 2020. LNCS, vol. 11962, pp. 451–462. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-37734-2_37
Ji, H., et al.: Point beyond class: A benchmark for weakly semi-supervised abnormality localization in chest x-rays. In: MICCAI. pp. 249–260. Springer (2022). https://doi.org/10.1007/978-3-031-16437-8_24
LaBonte, T., Song, Y., Wang, X., Vineet, V., Joshi, N.: Scaling novel object detection with weakly supervised detection transformers. In: WACV, pp. 85–96 (2023)
Qian, Z., et al.: Transformer based multiple instance learning for weakly supervised histopathology image segmentation. In: MICCAI, pp. 160–170. Springer Nature Switzerland Cham (2022). https://doi.org/10.1007/978-3-031-16434-7_16
Seo, J., Bae, W., Sutherland, D.J., Noh, J., Kim, D.: Object discovery via contrastive learning for weakly supervised object detection. In: ECCV, pp. 312–329. Springer (2022). https://doi.org/10.1007/978-3-031-19821-2_18
Shao, Z., Bian, H., Chen, Y., Wang, Y., Zhang, J., Ji, X., et al.: Transmil: transformer based correlated multiple instance learning for whole slide image classification. NeurIPS 34, 2136–2147 (2021)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Tang, P., Wang, X., Bai, X., Liu, W.: Multiple instance detection network with online instance classifier refinement. In: CVPR, pp. 2843–2851 (2017)
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H.: Training data-efficient image transformers & distillation through attention. In: ICML, pp. 10347–10357. PMLR (2021)
Wang, W., et al.: Pvtv 2: Improved baselines with pyramid vision transformer (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Basu, S., Papanai, A., Gupta, M., Gupta, P., Arora, C. (2023). Gall Bladder Cancer Detection from US Images with only Image Level Labels. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14220. Springer, Cham. https://doi.org/10.1007/978-3-031-43907-0_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-43907-0_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43906-3
Online ISBN: 978-3-031-43907-0
eBook Packages: Computer ScienceComputer Science (R0)
