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Multi-scale Attention Network for Diabetic Retinopathy Classification

Mohammad T. Al-Antary, Yasmine Arafa
2021 IEEE Access  
The model has been trained using both supervised data and weakly annotated data to boost the classification performance.  ...  A complication of diabetes may cause the blood vessels of the retina to swell and leak fluids and blood, which is called Diabetic Retinopathy (DR) [4] , [5] .  ... 
doi:10.1109/access.2021.3070685 fatcat:6766xdkp5bb3hiklaand5jy5ke

Vision Transformers in Medical Computer Vision – A Contemplative Retrospection [article]

Arshi Parvaiz, Muhammad Anwaar Khalid, Rukhsana Zafar, Huma Ameer, Muhammad Ali, Muhammad Moazam Fraz
2022 arXiv   pre-print
multiple medical imaging modalities that greatly assist in medical diagnosis and hence treatment process.  ...  These computer vision algorithms are being practised in medical image analysis and are transfiguring the perception and interpretation of Imaging data.  ...  The proposed method CLAM is a weakly supervised algorithm, it uses an attention module to determine the regions, and classify the cancer type.  ... 
arXiv:2203.15269v1 fatcat:wecjpoikbvfz5cygytqpktoxdq

Machine Learning Techniques for Ophthalmic Data Processing: A Review

Mhd Hasan Sarhan, Mohammad Ali Nasseri, Daniel Zapp, Mathias Maier, Chris Lohmann, Nassir Navab, Abouzar Eslami
2020 IEEE journal of biomedical and health informatics  
Machine learning approaches that use eye measurements and visual field data for glaucoma detection are also included in the survey.  ...  Three diseases are addressed in this survey, namely diabetic retinopathy, age-related macular degeneration, and glaucoma.  ...  and grading results for weakly supervised lesion localization are discussed.  ... 
doi:10.1109/jbhi.2020.3012134 pmid:32750971 fatcat:f4mmjk2ferduzbkpza4hoizzjq

Diabetic Retinopathy Diagnosis from Fundus Images Using Stacked Generalization of Deep Models

Harshit Kaushik, Dilbag Singh, Manjit Kaur, Hammam Alshazly, Atef Zaguia, Habib Hamam
2021 IEEE Access  
[10] proposed a multiple instance learning technique, which is a weakly supervised technique to detect DR in fundus images.  ...  The majority vote will result in an ensemble decision for class Figure 6 . 6 Sample fundus images from the Eye Pacs dataset used for the proposed Diabetic retinopathy diagnosis system.  ... 
doi:10.1109/access.2021.3101142 fatcat:ggq37s4qxrah3hrmhaq7ejh2ii

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
provide promising directions for the Medical Imaging community to fully harness Deep Learning in the future.  ...  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.  ...  [71] took a data driven approach using deep learning to classify Diabetic retinopathy (DR) in color fundus images.  ... 
arXiv:1902.05655v1 fatcat:mjplenjrprgavmy5ssniji4cam

Deep Neural Architectures for Medical Image Semantic Segmentation: Review

Muhammad Zubair Khan, Mohan Kumar Gajendran, Yugyung Lee, Muazzam A. Khan
2021 IEEE Access  
The authors in [40] discussed deep architectures with weakly supervised, fully supervised, and transfer learning techniques. It also uncovers the related datascarcity and class-imbalance problems.  ...  The model contained multi-scale kernels to aggregate the mappings from different size kernels for capturing more context information.  ... 
doi:10.1109/access.2021.3086530 fatcat:hacpqwdxybh63j5ygebqszm7qq

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

Fouzia Altaf, Syed M S Islam, Naveed Akhtar, Naeem Khalid Janjua
2019 IEEE Access  
This enables us to single out "lack of appropriately annotated large-scale data sets" as the core challenge (among other challenges) in this research direction.  ...  promising directions for the Medical Imaging Community to fully harness deep learning in the future.  ...  [75] used a CNN based technique to detect Diabetic Retinopathy (DR) in fundus images.  ... 
doi:10.1109/access.2019.2929365 fatcat:arimcbjaxrd3zcsjyzd7abjgd4

A Survey of Deep Learning-based Object Detection

Licheng Jiao, Fan Zhang, Fang Liu, Shuyuan Yang, Lingling Li, Zhixi Feng, Rong Qu
2019 IEEE Access  
With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved.  ...  the purpose of locating instances of semantic objects of a certain class.  ...  Annotating a bounding box for each object in large datasets is expensive, laborious and impractical. Weakly supervised learning relies on incomplete annotated training data to learn detection models.  ... 
doi:10.1109/access.2019.2939201 fatcat:jesz2av2tjbkxfpaqyecptgls4

Multiple instance learning: A survey of problem characteristics and applications

Marc-André Carbonneau, Veronika Cheplygina, Eric Granger, Ghyslain Gagnon
2018 Pattern Recognition  
Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag.  ...  This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data.  ...  In many problems, the numbers of negative and positive instances are severely imbalanced, and yet, the existing learning methods for imbalanced data set have not studied extensively in MIL.  ... 
doi:10.1016/j.patcog.2017.10.009 fatcat:5zqn7cyly5e4jnxeh4h57ipzta

Artificial Intelligence in Causality Healthcare Sector

Anandakumar Haldorai, Shrinand Anandakumar
2020 Journal of Computing in Engineering  
Comprehensive AI handles the implementation of traceability and transparency of statistical black box techniques of Machine Learning (ML), certainly Deep Learning (DL).  ...  Based on the approach of this paper, it can be argued that there is need for researchers to go beyond the comprehensive AI.  ...  The weakly supervised learning is a collective terminology used for the different techniques of structuring predictive frameworks by mastering the weak supervisions.  ... 
doi:10.46532/jce.20200704 fatcat:asdli23norgqjg6x6wzwnzqrry

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
It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing.  ...  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  ...  [87] , and diabetic retinopathy grading [88] , [89] .  ... 
arXiv:2008.09104v1 fatcat:z2gic7or4vgnnfcf4joimjha7i

Opportunities and obstacles for deep learning in biology and medicine

Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin, Brian T. Do, Gregory P. Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M. Hoffman, Wei Xie, Gail L. Rosen (+24 others)
2018 Journal of the Royal Society Interface  
Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields.  ...  Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records.  ...  for clarifying edits to the abstract and introduction and Robert Gieseke, Ruibang Luo, Stephen Ra, Sourav Singh and GitHub user snikumbh for correcting typos, formatting and references.  ... 
doi:10.1098/rsif.2017.0387 pmid:29618526 pmcid:PMC5938574 fatcat:65o4xmp53nc6zmj37srzuht6tq

Automatic Diagnosis of Pneumothorax From Chest Radiographs: A Systematic Literature Review

Tahira Iqbal, Arslan Shaukat, Muhammad Usman Akram, Zartasha Mustansar, Aimal Khan
2021 IEEE Access  
Among various medical imaging tools, chest radiographs are the most important and widely used diagnostic tool for the detection of thoracic pathologies.  ...  So far, best results have been achieved by deep-learning based models with Area-underreceiver-operating-characteristic-curve (AUC) of 88.87% for classification, and Dice-similarity-coefficient (DSC) of  ...  NIH AUC= 88.87 Multi resolution and Multi instance learning No proper explanation for class imbalance NIH Classification: AUC=80.5 Localization: DSC=3.9 (𝑟 0 =0) VGG16, data augmentation, STN No proper  ... 
doi:10.1109/access.2021.3122998 fatcat:67uv5nnle5hf7c2yjaypal57vi

A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges [article]

Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
2021 arXiv   pre-print
Bayesian approximation and ensemble learning techniques are two most widely-used UQ methods in the literature.  ...  This study reviews recent advances in UQ methods used in deep learning. Moreover, we also investigate the application of these methods in reinforcement learning (RL).  ...  In addition, they used a real-world case study, i.e., diabetic retinopathy diagnosis, to evaluate their method. Subedar et al.  ... 
arXiv:2011.06225v4 fatcat:wwnl7duqwbcqbavat225jkns5u

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Xiaochun Cao, Abbas Khosravi, U. Rajendra Acharya, Vladimir Makarenkov, Saeid Nahavandi
2021 Information Fusion  
Bayesian approximation and ensemble learning techniques are two widely-used types of uncertainty quantification (UQ) methods.  ...  This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlights fundamental research challenges and directions  ...  Acknowledgment This work was partially supported by the Australian Research Council's Discovery Projects funding scheme (project DP190102181) and the Natural Sciences and Engineering Research Council of  ... 
doi:10.1016/j.inffus.2021.05.008 fatcat:yschhguyxbfntftj6jv4dgywxm
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