1 code implementation • 14 Oct 2023 • Dongao Ma, Jiaxuan Pang, Michael B. Gotway, Jianming Liang
To realize this vision, we have developed Ark, a framework that accrues and reuses knowledge from heterogeneous expert annotations in various datasets.
1 code implementation • 27 Sep 2023 • Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway, Jianming Liang
Human anatomy is the foundation of medical imaging and boasts one striking characteristic: its hierarchy in nature, exhibiting two intrinsic properties: (1) locality: each anatomical structure is morphologically distinct from the others; and (2) compositionality: each anatomical structure is an integrated part of a larger whole.
1 code implementation • CVPR 2022 • Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Michael B. Gotway, Jianming Liang
Discriminative learning, restorative learning, and adversarial learning have proven beneficial for self-supervised learning schemes in computer vision and medical imaging.
1 code implementation • 15 Apr 2022 • Mohammad Reza Hosseinzadeh Taher, Fatemeh Haghighi, Michael B. Gotway, Jianming Liang
Our extensive experiments demonstrate that CAiD (1) enriches representations learned from existing instance discrimination methods; (2) delivers more discriminative features by adequately capturing finer contextual information from individual medial images; and (3) improves reusability of low/mid-level features compared to standard instance discriminative methods.
2 code implementations • 12 Aug 2021 • Mohammad Reza Hosseinzadeh Taher, Fatemeh Haghighi, Ruibin Feng, Michael B. Gotway, Jianming Liang
Transfer learning from supervised ImageNet models has been frequently used in medical image analysis.
4 code implementations • 21 Feb 2021 • Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Zongwei Zhou, Michael B. Gotway, Jianming Liang
This paper introduces a new concept called "transferable visual words" (TransVW), aiming to achieve annotation efficiency for deep learning in medical image analysis.
2 code implementations • 14 Jul 2020 • Fatemeh Haghighi, Mohammad Reza Hosseinzadeh Taher, Zongwei Zhou, Michael B. Gotway, Jianming Liang
To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a semantics-enriched, general-purpose, pre-trained 3D model, named Semantic Genesis.
Ranked #1 on Lung Nodule Detection on LUNA2016 FPRED
1 code implementation • 9 Apr 2020 • Zongwei Zhou, Vatsal Sodha, Jiaxuan Pang, Michael B. Gotway, Jianming Liang
Transfer learning from natural images to medical images has been established as one of the most practical paradigms in deep learning for medical image analysis.
no code implementations • 30 Mar 2020 • Germán González, Daniel Jimenez-Carretero, Sara Rodríguez-López, Carlos Cano-Espinosa, Miguel Cazorla, Tanya Agarwal, Vinit Agarwal, Nima Tajbakhsh, Michael B. Gotway, Jianming Liang, Mojtaba Masoudi, Noushin Eftekhari, Mahdi Saadatmand, Hamid-Reza Pourreza, Patricia Fraga-Rivas, Eduardo Fraile, Frank J. Rybicki, Ara Kassarjian, Raúl San José Estépar, Maria J. Ledesma-Carbayo
Objective: To generate a database of annotated computed tomography pulmonary angiographies, use it to compare the sensitivity and false positive rate of current algorithms and to develop new methods that improve such metrics.
2 code implementations • 19 Aug 2019 • Zongwei Zhou, Vatsal Sodha, Md Mahfuzur Rahman Siddiquee, Ruibin Feng, Nima Tajbakhsh, Michael B. Gotway, Jianming Liang
More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.
Ranked #1 on Pulmonary Embolism Detection on PE-CAD FPRED
1 code implementation • ICCV 2019 • Md Mahfuzur Rahman Siddiquee, Zongwei Zhou, Nima Tajbakhsh, Ruibin Feng, Michael B. Gotway, Yoshua Bengio, Jianming Liang
Qualitative and quantitative evaluations demonstrate that the proposed method outperforms the state of the art in multi-domain image-to-image translation and that it surpasses predominant weakly-supervised localization methods in both disease detection and localization.
1 code implementation • 3 Feb 2018 • Zongwei Zhou, Jae Y. Shin, Suryakanth R. Gurudu, Michael B. Gotway, Jianming Liang
The splendid success of convolutional neural networks (CNNs) in computer vision is largely attributable to the availability of massive annotated datasets, such as ImageNet and Places.
no code implementations • 2 Jun 2017 • Nima Tajbakhsh, Jae Y. Shin, Suryakanth R. Gurudu, R. Todd Hurst, Christopher B. Kendall, Michael B. Gotway, Jianming Liang
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence.