Filters








151 Hits in 8.3 sec

Prostate cancer inference via weakly-supervised learning using a large collection of negative MRI [article]

Ruiming Cao, Xinran Zhong, Fabien Scalzo, Steven Raman, Kyung hyun Sung
2019 arXiv   pre-print
Here, we propose the baseline MRI model to alternatively learn the appearance of mp-MRI using radiology-confirmed negative MRI cases via weakly supervised learning.  ...  We trained and validated the baseline MRI model using 1,145 negative prostate mp-MRI scans. For evaluation, we used separated 232 mp-MRI scans, consisting of both positive and negative MRI cases.  ...  Conclusion We proposed the baseline MRI model via weakly supervised learning using a large collection of negative mp-MRI cases.  ... 
arXiv:1910.02185v1 fatcat:av53gmg23rhiddhskvph64k5pm

Artificial Intelligence in Quantitative Ultrasound Imaging: A Review [article]

Boran Zhou, Xiaofeng Yang, Tian Liu
2020 arXiv   pre-print
However, no research has been found that surveyed the AI use in QUS. The purpose of this paper is to review recent research into the AI applications in QUS.  ...  Quantitative ultrasound (QUS) imaging is a reliable, fast and inexpensive technique to extract physically descriptive parameters for assessing pathologies.  ...  Transfer learning and weakly supervised learning have been used to address this challenge.  ... 
arXiv:2003.11658v1 fatcat:iujuh7gra5ax7od2gxoo6yrbpe

Deep Learning in Multi-organ Segmentation [article]

Yang Lei, Yabo Fu, Tonghe Wang, Richard L.J. Qiu, Walter J. Curran, Tian Liu, Xiaofeng Yang
2020 arXiv   pre-print
This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and applications.  ...  We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multiorgan segmentation using benchmark datasets, including the 2017 AAPM Thoracic Auto-segmentation Challenge  ...  ACKNOWLEDGEMENT This research was supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718 and Emory Winship Cancer Institute pilot grant.  ... 
arXiv:2001.10619v1 fatcat:6uwqwnzydzccblh5cajhsgdpea

A weakly supervised registration-based framework for prostate segmentation via the combination of statistical shape model and CNN [article]

Chunxia Qin, Xiaojun Chen, Jocelyne Troccaz
2020 arXiv   pre-print
In conclusion, via the combination of two weakly supervised neural networks, our segmentation method might be an effective and robust approach for prostate segmentation.  ...  To address this problem, we proposed a weakly supervised registration-based framework for the precise prostate segmentation, by combining convolutional neural network (CNN) with statistical shape model  ...  In this paper, based on a registration-based approach, we proposed a weakly supervised segmentation framework to tackle the prostate extraction problem, via a boundary predictor (SSM-Net) and a label classifier  ... 
arXiv:2007.11726v2 fatcat:2nczf7k6kvdhdm56lhqscqreue

End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive Reduction [article]

Anindo Saha, Matin Hosseinzadeh, Henkjan Huisman
2021 arXiv   pre-print
Using a large dataset of 1950 prostate bpMRI paired with radiologically-estimated annotations, we hypothesize that such CNN-based models can be trained to detect biopsy-confirmed malignancies in an independent  ...  We present a multi-stage 3D computer-aided detection and diagnosis (CAD) model for automated localization of clinically significant prostate cancer (csPCa) in bi-parametric MR imaging (bpMRI).  ...  Acknowledgements The authors would like to acknowledge Maarten de Rooij and Ilse Slootweg from Radboud University Medical Center for the annotation of fully delineated masks of prostate cancer for every  ... 
arXiv:2101.03244v10 fatcat:l3nmcrhgsjfspjznv6urmotw7a

End-to-end prostate cancer detection in bpMRI via 3D CNNs: effects of attention mechanisms, clinical priori and decoupled false positive reduction

Anindo Saha, Matin Hosseinzadeh, Henkjan Huisman
2021 Medical Image Analysis  
Using a large dataset of 1950 prostate bpMRI paired with radiologically-estimated annotations, we hypothesize that such CNN-based models can be trained to detect biopsy-confirmed malignancies in an independent  ...  We present a multi-stage 3D computer-aided detection and diagnosis (CAD) model2 for automated localization of clinically significant prostate cancer (csPCa) in bi-parametric MR imaging (bpMRI).  ...  Acknowledgements The authors would like to acknowledge Maarten de Rooij and Ilse Slootweg from Radboud University Medical Center for the annotation of fully delineated masks of prostate cancer for every  ... 
doi:10.1016/j.media.2021.102155 pmid:34245943 fatcat:izmd6e4kdbd55ccjsfa7kpn4w4

Deep learning in medical imaging and radiation therapy

Berkman Sahiner, Aria Pezeshk, Lubomir M. Hadjiiski, Xiaosong Wang, Karen Drukker, Kenny H. Cha, Ronald M. Summers, Maryellen L. Giger
2018 Medical Physics (Lancaster)  
The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies  ...  for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.  ...  , and weakly supervised methods, that use noisy labels, or images labeled as positive or negative, without localization information, to train for a specific task.  ... 
doi:10.1002/mp.13264 pmid:30367497 fatcat:bottst5mvrbkfedbuocbrstcnm

A Dilated Residual Hierarchically Fashioned Segmentation Framework for Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide Images [article]

Taimur Hassan and Bilal Hassan and Ayman El-Baz and Naoufel Werghi
2021 arXiv   pre-print
Prostate cancer (PCa) is the second deadliest form of cancer in males, and it can be clinically graded by examining the structural representations of Gleason tissues.  ...  In addition to this, the proposed framework has been extensively evaluated on a large-scale PCa dataset containing 10,516 whole slide scans (with around 71.7M patches), where it outperforms state-of-the-art  ...  [31] conducted a study to showcase the capacity of deep learning systems for the identification of PCa (using mp-MRI) as compared to the conventional non-deep learning schemes.  ... 
arXiv:2011.00527v5 fatcat:japueayqljcl3jicoxmn5z43tu

A review of deep learning in medical imaging: Image traits, technology trends, case studies with progress highlights, and future promises [article]

S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers
2020 arXiv   pre-print
We conclude with a discussion and presentation of promising future directions.  ...  Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called  ...  Weakly or partially supervised learning. In [54] , Wang et al. solve a weakly-supervised multi-label disease classification from a chest x-ray.  ... 
arXiv:2008.09104v1 fatcat:z2gic7or4vgnnfcf4joimjha7i

Deep Learning in Medical Image Registration: A Review [article]

Yabo Fu, Yang Lei, Tonghe Wang, Walter J. Curran, Tian Liu, Xiaofeng Yang
2019 arXiv   pre-print
This paper presents a review of deep learning (DL) based medical image registration methods.  ...  Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of development in medical image registration using deep learning.  ...  Award, a philanthropic award provided by the Winship Cancer Institute of Emory University.  ... 
arXiv:1912.12318v1 fatcat:kuvckosqd5hp7asg6dofhuiis4

Algorithm Fairness in AI for Medicine and Healthcare [article]

Richard J. Chen, Tiffany Y. Chen, Jana Lipkova, Judy J. Wang, Drew F.K. Williamson, Ming Y. Lu, Sharifa Sahai, Faisal Mahmood
2022 arXiv   pre-print
Lastly, we also review emerging technology for mitigating bias via federated learning, disentanglement, and model explainability, and their role in AI-SaMD development.  ...  In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care.  ...  In radiology, federated learning has been recently used for multi-institutional collaboration and validation of AI algorithms for prostate segmentation, brain cancer detection, and monitoring Alzheimer's  ... 
arXiv:2110.00603v2 fatcat:pspb6bqqxjh45an5mhqohysswu

Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration [article]

Bo Zhou, Zachary Augenfeld, Julius Chapiro, S. Kevin Zhou, Chi Liu, James S. Duncan
2021 arXiv   pre-print
One can train a deep learning-based anatomy extractor, but it requires large-scale manual annotations on specific modalities, which are often extremely time-consuming to obtain and require expert radiological  ...  Multimodal image registration has many applications in diagnostic medical imaging and image-guided interventions, such as Transcatheter Arterial Chemoembolization (TACE) of liver cancer guided by intraprocedural  ...  Acknowledgments This work was supported by funding from the National Institutes of Health/ National Cancer Institute (NIH/NCI) under grant number R01CA206180.  ... 
arXiv:2104.07056v1 fatcat:dyayv5qbyzhofgxbi7vgoq4ama

Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation [article]

Nima Tajbakhsh, Laura Jeyaseelan, Qian Li, Jeffrey Chiang, Zhihao Wu, Xiaowei Ding
2020 arXiv   pre-print
Recently, a large body of research has studied the problem of medical image segmentation with imperfect datasets, tackling two major dataset limitations: scarce annotations where only limited annotated  ...  In this article, we provide a detailed review of the solutions above, summarizing both the technical novelties and empirical results.  ...  The authors use a histopathology image dataset of colon cancer, consisting of 330 cancer and 580 non-cancer images, of which 250 cancer and 500 non-cancer images are used for training; 80 cancer and 80  ... 
arXiv:1908.10454v2 fatcat:mjvfbhx75bdkbheysq3r7wmhdi

Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions [article]

Fouzia Altaf, Syed M. S. Islam, Naveed Akhtar, Naeem K. Janjua
2019 arXiv   pre-print
This enables us to single out 'lack of appropriately annotated large-scale datasets' as the core challenge (among other challenges) in this research direction.  ...  This article does not assume prior knowledge of Deep Learning and makes a significant contribution in explaining the core Deep Learning concepts to the non-experts in the Medical community.  ...  [194] presented a deep learning method using Recurrent Inference Machines (RIM) for the reconstruction of MRI.  ... 
arXiv:1902.05655v1 fatcat:mjplenjrprgavmy5ssniji4cam

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond

Guang Yang, Qinghao Ye, Jun Xia
2021 Information Fusion  
Many of the machine learning algorithms cannot manifest how and why a decision has been cast. This is particularly true of the most popular deep neural network approaches currently in use.  ...  Comprehensive quantitative and qualitative analyses can prove the efficacy of our proposed XAI solutions, from which we can envisage successful applications in a broader range of clinical questions.  ...  It is of note that some hybrid methods, e.g., semi-supervised learning (using partially labelled data) and weakly supervised (using indirect labels), are also under development.  ... 
doi:10.1016/j.inffus.2021.07.016 pmid:34980946 pmcid:PMC8459787 fatcat:3rmzvn72dbgglcddgolce2xsfe
« Previous Showing results 1 — 15 out of 151 results