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Segmentation of anatomical structures and pathological regions in medical images is essential for modern clinical diagnosis, disease research, and treatment planning.
A threshold selection method from gray-level histograms
Otsu, N., 1979 · 1979
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Assessing the (un)trustworthiness of saliency maps for localizing abnormalities in medical imaging
Arun, N., Gaw, N., Singh, P., Chang, K., Aggarwal, M., Chen, B., Hoebel, K., Gupta, S., Patel, J., Gidwani, M., Adebayo, J., Li, M.D., Kalpathy-Cramer, J., 2021 · 2008
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Contrastive learning with hard negative samples
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Medical big data: neurological diseases diagnosis through medical data analysis
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Cheng, J., 2017 · 2017
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Radiology objects in context (roco): A multimodal image dataset, in: CVII-STENT/LABELS@MICCAI
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Dataset of breast ultrasound images
Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A., 2020 · 2019
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Rethinking class activation mapping for weakly supervised object localization, in: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XV 16, Springer. pp. 618–634
Bae, W., Noh, J., Kim, G., 2020 · 2020
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Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network
Byra, M., Jarosik, P., Szubert, A., Galperin, M., Ojeda-Fournier, H., Olson, L., O’Boyle, M., Comstock, C., Andre, M., 2020 · 2020
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Big self-supervised models are strong semi-supervised learners
Chen, T., Kornblith, S., Swersky, K., Norouzi, M., Hinton, G.E., 2020 · 2020
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Can ai help in screening viral and covid-19 pneumonia?
Chowdhury, M.E.H., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M.A., Mahbub, Z.B., Islam, K.R., Khan, M.S., Iqbal, A., Emadi, N.A., Reaz, M.B.I., Islam, M.T., 2020 · 2020
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CT lung & heart & trachea segmentation
Konya, D., 2020 · 2020
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
Liu, J., Lin, Z., Padhy, S., Tran, D., Bedrax Weiss, T., Lakshminarayanan, B., 2020 · 2020
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A general framework for uncertainty estimation in deep learning
Loquercio, A., Segu, M., Scaramuzza, D., 2020 · 2020
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nnu-net: a self-configuring method for deep learning-based biomedical image segmentation
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H., 2021 · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I., 2021 · 2021
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Exploring the effect of image enhancement techniques on covid-19 detection using chest x-ray images
Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Abul Kashem, S.B., Islam, M.T., Al Maadeed, S., Zughaier, S.M., Khan, M.S., Chowdhury, M.E., 2021 · 2021
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Multimodal self-supervised learning for medical image analysis, in: International conference on information processing in medical imaging, Springer. pp. 661–673
Taleb, A., Lippert, C., Klein, T., Nabi, M., 2021 · 2021
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gscorecam: What is clip looking at?, in: Proceedings of the Asian Conference on Computer Vision (ACCV)
Chen, P., Li, Q., Biaz, S., Bui, T., Nguyen, A., 2022 · 2022
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Decoupling zero-shot semantic segmentation
Ding, J., Xue, N., Xia, G.S., Dai, D., 2022 · 2022
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Denseclip: Language-guided dense prediction with context-aware prompting, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 18082–18091
Rao, Y., Zhao, W., Chen, G., Tang, Y., Zhu, Z., Huang, G., Zhou, J., Lu, J., 2022 · 2022
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Expert-level detection of pathologies from unannotated chest x-ray images via self-supervised learning
Tiu, E., Talius, E., Patel, P., Langlotz, C.P., Ng, A.Y., Rajpurkar, P., 2022 · 2022
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Medclip: Contrastive learning from unpaired medical images and text
Wang, Z., Wu, Z., Agarwal, D., Sun, J., 2022 · 2022
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Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., Dollár, P., Girshick, R., 2023 · 2023
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Clip surgery for better explainability with enhancement in open-vocabulary tasks
Li, Y., Wang, H., Duan, Y., Li, X., 2023 · 2023
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A chatgpt aided explainable framework for zero-shot medical image diagnosis
Liu, J., Hu, T., Zhang, Y., Gai, X., Feng, Y., Liu, Z., 2023 · 2023
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Segment anything in medical images
Ma, J., Wang, B., 2023 · 2023
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Exploring transfer learning in medical image segmentation using vision-language models
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Yeh, C.H., Hong, C.Y., Hsu, Y.C., Liu, T.L., Chen, Y., LeCun, Y., 2022 · 2022
Cited alongside, same era.
Efficient bayesian uncertainty estimation for nnu-net, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. pp. 535–544
Zhao, Y., Yang, C., Schweidtmann, A., Tao, Q., 2022 · 2022
Cited alongside, same era.
Extract free dense labels from clip, in: European Conference on Computer Vision, Springer. pp. 696–712
Zhou, C., Loy, C.C., Dai, B., 2022 · 2022
Cited alongside, same era.
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al., 2023 · 2023
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Efficient self-supervised learning with contextualized target representations for vision, speech and language, in: International Conference on Machine Learning, PMLR. pp. 1416–1429
Baevski, A., Babu, A., Hsu, W.N., Auli, M., 2023 · 2023
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Universeg: Universal medical image segmentation
Butoi, V.I., Ortiz, J.J.G., Ma, T., Sabuncu, M.R., Guttag, J., Dalca, A.V., 2023 · 2023
Cited alongside, same era.
Segment anything model (sam) enhanced pseudo labels for weakly supervised semantic segmentation
Chen, T., Mai, Z., Li, R., lun Chao, W., 2023 · 2023
Cited alongside, same era.
Sam on medical images: A comprehensive study on three prompt modes
Cheng, D., Qin, Z., Jiang, Z., Zhang, S., Lao, Q., Li, K., 2023 · 2023
Cited alongside, same era.
Poudel, K., Dhakal, M., Bhandari, P., Adhikari, R., Thapaliya, S., Khanal, B., 2023 · 2023
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Filtering, distillation, and hard negatives for vision-language pre-training
Radenovic, F., Dubey, A., Kadian, A., Mihaylov, T., Vandenhende, S., Patel, Y., Wen, Y., Ramanathan, V., Mahajan, D., 2023 · 2023
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Autosam: Adapting sam to medical images by overloading the prompt encoder
Shaharabany, T., Dahan, A., Giryes, R., Wolf, L., 2023 · 2023
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Vision-language modelling for radiological imaging and reports in the low data regime
Windsor, R., Jamaludin, A., Kadir, T., Zisserman, A., 2023 · 2023
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Foundation model assisted weakly supervised semantic segmentation
Yang, X., Gong, X., 2023 · 2023
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Large-scale domain-specific pretraining for biomedical vision-language processing
Zhang, S., Xu, Y., Usuyama, N., Bagga, J., Tinn, R., Preston, S., Rao, R., Wei, M., Valluri, N., Wong, C., Lungren, M.P., Naumann, T., Poon, H., 2023 · 2023
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Test-time adaptation with salip: A cascade of sam and clip for zero shot medical image segmentation
Aleem, S., Wang, F., Maniparambil, M., Arazo, E., Dietlmeier, J., Silvestre, G., Curran, K., O’Connor, N.E., Little, S., 2024 · 2024
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Protosam-one shot medical image segmentation with foundational models
Ayzenberg, L., Giryes, R., Greenspan, H., 2024 · 2024
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Un-sam: Universal prompt-free segmentation for generalized nuclei images
Chen, Z., Xu, Q., Liu, X., Yuan, Y., 2024 · 2024
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Samaug: Point prompt augmentation for segment anything model
Dai, H., Ma, C., Yan, Z., Liu, Z., Shi, E., Li, Y., Shu, P., Wei, X., Zhao, L., Wu, Z., Zeng, F., Zhu, D., Liu, W., Li, Q., Sun, L., Liu, S.Z.T., Li, X., 2024 · 2024
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Featup: A model-agnostic framework for features at any resolution
Fu, S., Hamilton, M., Brandt, L., Feldman, A., Zhang, Z., Freeman, W.T., 2024 · 2024
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Clipsam: Clip and sam collaboration for zero-shot anomaly segmentation
Li, S., Cao, J., Ye, P., Ding, Y., Tu, C., Chen, T., 2024 · 2024
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Weakly supervised salient object detection via bounding-box annotation and sam model
Liu, X., Huang, X., 2024 · 2024
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Medpix 2.0: a comprehensive multimodal biomedical dataset for advanced ai applications
Siragusa, I., Contino, S., La Ciura, M., Alicata, R., Pirrone, R., 2024 · 2024
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Visual explanations of image-text representations via multi-modal information bottleneck attribution
Wang, Y., Rudner, T.G.J., Wilson, A.G., 2024 · 2024
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