A Few-shot approach to MRI-based Knee Disorder Diagnosis using Fuzzy Layers
Authors: 
Mohana Singh, K Ram Prabhakar, Viswanath Pamulakanty Sudarshan, Murali Poduval, 
Arpan Pal, Jayavardhana GubbiAuthors Info & Claims
Article No.: 52, Pages 1 - 9
Abstract
Magnetic Resonance Imaging (MRI) has a long-standing acceptance as the de facto technology for obtaining high-resolution three-dimensional images of soft tissue pathologies of the knee. However, the volume and amount of information in MRI sequences warrant a need for automated interpretations that can assist radiologists in making faster and more consistent diagnoses. Despite the popularity of deep learning in achieving this goal, it is limited by the dependency on large amounts of data. This work explores the detection of knee injury classes from a data-efficiency perspective. It also attempts to incorporate soft decision-making that is prevalent in the medical diagnosis domain. First, a training regime utilizing knowledge transferred from related diagnoses and a pool of unlabeled data is proposed. Secondly, to aid the deep-learning-based model with soft computing elements, novel fuzzy layers are employed along with the proposed training regime. Experiments are performed on multiple base models and compared with existing baselines. Results show that the proposed approach elevates the base models’ performance substantially compared to all the baselines.
References
[1]
Derek T Anderson, Grant J Scott, Muhammad Aminul Islam, Bryce Murray, and Richard Marcum. 2018. Fuzzy choquet integration of deep convolutional neural networks for remote sensing. In Computational Intelligence for Pattern Recognition. Springer, 1–28.
[2]
Nicholas Bien, Pranav Rajpurkar, Robyn L Ball, Jeremy Irvin, Allison Park, Erik Jones, Michael Bereket, Bhavik N Patel, Kristen W Yeom, Katie Shpanskaya, 2018. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLoS medicine 15, 11 (2018), e1002699.
[3]
Iris Dominguez-Catena, Daniel Paternain, and Mikel Galar. 2020. Additional feature layers from ordered aggregations for deep neural networks. In 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 1–8.
[4]
Xiaoxiao Du and Alina Zare. 2018. Multiple instance choquet integral classifier fusion and regression for remote sensing applications. IEEE Transactions on Geoscience and Remote Sensing 57, 5 (2018), 2741–2753.
[5]
Matteo Dunnhofer, Niki Martinel, and Christian Micheloni. 2021. Improving MRI-based knee disorder diagnosis with pyramidal feature details. In Medical Imaging with Deep Learning. PMLR, 131–147.
[6]
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun. 2017. Dermatologist-level classification of skin cancer with deep neural networks. nature 542, 7639 (2017), 115–118.
[7]
Rotem Golan, Christian Jacob, and Jörg Denzinger. 2016. Lung nodule detection in CT images using deep convolutional neural networks. In 2016 international joint conference on neural networks (IJCNN). IEEE, 243–250.
[8]
Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams, Jorge Cuadros, 2016. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama 316, 22 (2016), 2402–2410.
[9]
Kurt Hornik, Maxwell Stinchcombe, and Halbert White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2, 5 (1989), 359–366.
[10]
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems, F. Pereira, C.J. Burges, L. Bottou, and K.Q. Weinberger (Eds.). Vol. 25. Curran Associates, Inc.https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
[11]
Dong-Hyun Lee 2013. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on challenges in representation learning, ICML, Vol. 3. 896.
[12]
Stanton R Price, Steven R Price, and Derek T Anderson. 2019. Introducing fuzzy layers for deep learning. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 1–6.
[13]
Sachin Ravi and Hugo Larochelle. 2017. Optimization as a model for few-shot learning. In International conference on learning representations.
[14]
Grant J Scott, Kyle C Hagan, Richard A Marcum, James Alex Hurt, Derek T Anderson, and Curt H Davis. 2018. Enhanced fusion of deep neural networks for classification of benchmark high-resolution image data sets. IEEE Geoscience and Remote Sensing Letters 15, 9 (2018), 1451–1455.
[15]
Grant J Scott, Richard A Marcum, Curt H Davis, and Tyler W Nivin. 2017. Fusion of deep convolutional neural networks for land cover classification of high-resolution imagery. IEEE Geoscience and Remote Sensing Letters 14, 9 (2017), 1638–1642.
[16]
Chen-Han Tsai, Nahum Kiryati, Eli Konen, Iris Eshed, and Arnaldo Mayer. 2020. Knee injury detection using MRI with efficiently-layered network (ELNet). In Medical Imaging with Deep Learning. PMLR, 784–794.
[17]
Ronald R Yager. 1988. On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on systems, Man, and Cybernetics 18, 1(1988), 183–190.
[18]
Ronald R Yager. 1993. Families of OWA operators. Fuzzy sets and systems 59, 2 (1993), 125–148.
[19]
Yuanhang Zheng, Zeshui Xu, and Xinxin Wang. 2021. The fusion of deep learning and fuzzy systems: A state-of-the-art survey. IEEE Transactions on Fuzzy Systems(2021).
Index Terms
- A Few-shot approach to MRI-based Knee Disorder Diagnosis using Fuzzy Layers
Recommendations
Brain tumor diagnosis using CT scan and MRI images based on a deep learning method based on VGG
Brain cancer is one of the most deadly forms of cancer today, and its timely and accurate diagnosis can significantly impact the patient’s quality of life. A computerized tomography scan (CT) and magnetic resonance imaging (MRI) of the brain is required ...
Glioma Diagnosis Aid through CNNs and Fuzzy-C Means for MRI
IJCCI 2019: Proceedings of the 11th International Joint Conference on Computational IntelligenceGlioma is a type of brain tumor that causes mortality in many cases. Early diagnosis is an important factor. Typically, it is detected through MRI and then either a treatment is applied, or it is removed through surgery. Deep-learning techniques are ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
December 2022
506 pages
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 12 May 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICVGIP'22
ICVGIP'22: Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing
December 8 - 10, 2022
Gandhinagar, India
Acceptance Rates
Overall Acceptance Rate 95 of 286 submissions, 33%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 40Total Downloads
- Downloads (Last 12 months)19
- Downloads (Last 6 weeks)3
Reflects downloads up to 19 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format