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Patient-level Prediction of Multi-classification Task at Prostate MRI based on End-to-End Framework learning from Diagnostic Logic of Radiologists

Lizhi Shao, Zhenyu Liu, Ye Yan, Jiangang Liu, Xiongjun Ye, Haizhui Xia, Xuehua Zhu, Yuting Zhang, Zhiting Zhang, Huiying Chen, Wei He, Cheng Liu (+7 others)
2021 IEEE Transactions on Biomedical Engineering  
The grade groups (GGs) of Gleason scores (Gs) is the most critical indicator in the clinical diagnosis  ...  However, to our knowledge, no model is applying DRL to learn from diagnostic logic of radiologists for modeling, especially for multi-classification tasks at the patient-level.  ...  Our contributions are summarized as follows: 1) We design an end-to-end framework for patient-level multi-classification tasks, which diagnostic logic of radiologists was fully learned and applied in modeling  ... 
doi:10.1109/tbme.2021.3082176 fatcat:f5izpudvwfdzliemqtbo3twpf4

Patient-level Prediction of Multi-classification Task at Prostate MRI based on End-to-End Framework learning from Diagnostic Logic of Radiologists

Lizhi Shao, Zhenyu Liu, Ye Yan, Jiangang Liu, Xiongjun Ye, Haizhui Xia, Xuehua Zhu, Yuting Zhang, Zhiting Zhang, Huiying Chen, Wei He, Cheng Liu (+7 others)
2021 IEEE Transactions on Biomedical Engineering  
Our method reveals the state-of-the-art performance for patient-level multi-classification task to personalized medicine.  ...  Therefore, more domain knowledge (e.g. diagnostic logic of radiologists) needs to be incorporated into the design of the framework.  ...  However, to our knowledge, no model is applying DRL to learn from diagnostic logic of radiologists for modeling, especially for multi-classification tasks at the patient-level.  ... 
doi:10.1109/tbme.2021.3082176 pmid:34014820 fatcat:xuivj6lvanfiffj4kgvofpo3py

Unifying Neural Learning and Symbolic Reasoning for Spinal Medical Report Generation [article]

Zhongyi Han, Benzheng Wei, Yilong Yin, Shuo Li
2020 arXiv   pre-print
., given spinal medical images and directly create radiologist-level diagnosis reports to support clinical decision making, is a novel yet fundamental study in the domain of artificial intelligence in  ...  Generally speaking, the NSL framework firstly employs deep neural learning to imitate human visual perception for detecting abnormalities of target spinal structures.  ...  Introduction This paper devotes to the task of radiologist-level report generation based on spinal images in the field of spine radiology directly and automatically.  ... 
arXiv:2004.13577v1 fatcat:oh5aka5zr5be3ipd7qqnyhikzy

Table of Contents

2021 IEEE Transactions on Biomedical Engineering  
Ali 3681 Patient-Level Prediction of Multi-Classification Task at Prostate MRI Based on End-to-End Framework Learning From Diagnostic Logic of Radiologists . . . . . . . . . . . . . . . . . L.  ...  Balasubramanian 3620 Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M.  ... 
doi:10.1109/tbme.2021.3115371 fatcat:z3eyxr7ynfa65m56i46vw6ju6y

A survey on deep learning in medical image analysis

Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A.W.M. van der Laak, Bram van Ginneken, Clara I. Sánchez
2017 Medical Image Analysis  
We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area.  ...  This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year.  ...  ArXiv was searched for papers mentioning one of a set of terms related to medical imaging.  ... 
doi:10.1016/j.media.2017.07.005 pmid:28778026 fatcat:esbj72ftwvbgzh6jgw367k73j4

MUSKETEER D2.1 Industrial and Technical Requirements

Joao Correia
2019 Zenodo  
D2.1 is a report containing an exhaustive list of domain-specific and business requirements coming from the two scenarios and from other complementary domains, and assessment of available datasets.  ...  This report contains a complete specification of technical requirements to drive technical developments in MUSKETEER WPs 3,4,5,6 and WP7 integration.  ...  "Original multi-parametric MRI images of prostate", make available by I2CVB: http://i2cvb.github.io/.  ... 
doi:10.5281/zenodo.4697460 fatcat:so2lobraszaq5kr4ndh5wnmdwq

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 Sensors  
With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled.  ...  It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21144758 fatcat:jytyt4u2pjgvhnhcto3vcvd3a4

A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis [article]

Xiaozheng Xie, Jianwei Niu, Xuefeng Liu, Zhengsu Chen, Shaojie Tang, Shui Yu
2020 arXiv   pre-print
More recent works utilize the domain knowledge from medical doctors, to create networks that resemble how medical doctors are trained, mimic their diagnostic patterns, or focus on the features or areas  ...  In this survey, we summarize the current progress on integrating medical domain knowledge into deep learning models for various tasks, such as disease diagnosis, lesion, organ and abnormality detection  ...  The multi-task learning (MTL) framework is proposed to incorporate the above information into the main task of lung nodule classification.  ... 
arXiv:2004.12150v3 fatcat:2cqumcjkizgivmo67reznxacie

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  
Explainable Artificial Intelligence (XAI) is an emerging research topic of machine learning aimed at unboxing how AI systems' black-box choices are made.  ...  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.  ...  In particular, we found that overall our proposed method remained the best-performed algorithm with an AUC of 95.53% at the patient-level and 86.06% at the image-level.  ... 
doi:10.1016/j.inffus.2021.07.016 pmid:34980946 pmcid:PMC8459787 fatcat:3rmzvn72dbgglcddgolce2xsfe

Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies

Jake Kendrick, Roslyn Francis, Ghulam Mubashar Hassan, Pejman Rowshanfarzad, Robert Jeraj, Collin Kasisi, Branimir Rusanov, Martin Ebert
2021 Frontiers in Oncology  
On the other hand, artificial intelligence techniques such as deep learning can facilitate end-to-end automated feature extraction and model generation with minimal human intervention.  ...  Metastatic Prostate Cancer (mPCa) is associated with a poor patient prognosis. mPCa spreads throughout the body, often to bones, with spatial and temporal variations that make the clinical management of  ...  These techniques can be used to perform end-to-end predictive modelling tasks, encompassing automated hierarchical feature extraction and the utilisation of these features for the subsequent classification  ... 
doi:10.3389/fonc.2021.771787 pmid:34790581 pmcid:PMC8591174 fatcat:loi7fbwaynfcbltqfhj5nizybu

Transformers in Medical Image Analysis: A Review [article]

Kelei He, Chen Gan, Zhuoyuan Li, Islem Rekik, Zihao Yin, Wen Ji, Yang Gao, Qian Wang, Junfeng Zhang, Dinggang Shen
2022 arXiv   pre-print
Our paper aims to promote awareness and application of Transformers in the field of medical image analysis.  ...  We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis.  ...  This work was also supported in part by the grant from Jiangsu Provincial Key R&D Program under No. BE2020620 and BE2020723.  ... 
arXiv:2202.12165v2 fatcat:wjeuwhcu5ngybcia5k7lyxntgi

Artificial intelligence for clinical oncology

Benjamin H. Kann, Ahmed Hosny, Hugo J.W.L. Aerts
2021 Cancer Cell  
With recent advances in the field of artificial intelligence (AI), there is now a computational basis to integrate and synthesize this growing body of multi-dimensional data, deduce patterns, and predict  ...  outcomes to improve shared patient and clinician decision making.  ...  Union -European Research Council (H.J.W.L.A.: 866504), as well as the Radiological Society of North America (B.H.K.: RSCH2017).  ... 
doi:10.1016/j.ccell.2021.04.002 pmid:33930310 pmcid:PMC8282694 fatcat:k6jr3lfa2fe73l3nmq4ywjxg6q

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled.  ...  It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data.  ...  Finally, a maximum likelihood estimation based on the window-level prediction is adopted to determine if the entire EEG recording was recorded from a particular patient (i.e. subject prediction).  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Rogier R. Wildeboer, Ruud J.G. van Sloun, Hessel Wijkstra, Massimo Mischi
2020 Computer Methods and Programs in Biomedicine  
However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process.  ...  Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools.  ...  Acknowledgements This study has received funding from the Dutch Cancer Society (# UVA2013-5941 ) and a European Research Council Starting Grant (# 280209 ), and was performed within the framework of the  ... 
doi:10.1016/j.cmpb.2020.105316 pmid:31951873 fatcat:obbpitr6i5hmdpyx4afgunypxq

An overview of deep learning in medical imaging [article]

Imran Ul Haq
2022 arXiv   pre-print
, and registration), (iii) review seven main application fields of DL in medical imaging, (iv) give an initial stage to those keen on adding to the research area about DL in clinical imaging by providing  ...  difficulties, lessons learned and future of DL in the field of medical science.  ...  The key task in designing CBIR approaches is to derive efficient feature descriptions from information at the pixel level and associate them with realistic concepts.  ... 
arXiv:2202.08546v1 fatcat:tg32btcm5vdsnlzeuhdttozj6m
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