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Critical Assessment of Transfer Learning for Medical Image Segmentation with Fully Convolutional Neural Networks [article]

Davood Karimi, Simon K. Warfield, Ali Gholipour
2022 arXiv   pre-print
Transfer learning is widely used for training machine learning models. Here, we study the role of transfer learning for training fully convolutional networks (FCNs) for medical image segmentation.  ...  We observe that convolutional filters of an FCN change little during training for medical image segmentation, and still look random at convergence.  ...  Critical Assessment of Transfer Learning for Medical Image Segmentation with Fully Convolutional Neural Networks Davood Karimi, Simon K.  ... 
arXiv:2006.00356v2 fatcat:rxvz7zhgbnejhgk4nd2rguj4rq

Front Matter: Volume 10574

Proceedings of SPIE, Elsa D. Angelini, Bennett A. Landman
2018 Medical Imaging 2018: Image Processing  
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication.  ...  LSTM 10574 0B Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks 10574 0C An effective fully deep convolutional neural networks for mitochondria segmentation  ... 
doi:10.1117/12.2315755 fatcat:jdfbaent6vhu5dwlrqrqt66vce

Deep Neural Architectures for Medical Image Semantic Segmentation: Review

Muhammad Zubair Khan, Mohan Kumar Gajendran, Yugyung Lee, Muazzam A. Khan
2021 IEEE Access  
The study reviews the state-of-the-art deep learning techniques designed to perform medical image analysis; it also assesses critical challenges associated with medical diagnosis and provides future directions  ...  V-Net, a volumetric convolutional neural network, which is FCN for Volumetric Medical Image Segmentation, was proposed in [143] .  ... 
doi:10.1109/access.2021.3086530 fatcat:hacpqwdxybh63j5ygebqszm7qq

Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

Jwan Saeed, Subhi Zeebaree
2021 Journal of Applied Science and Technology Trends  
Attempts to overcome these challenges have been made by analyzing the images using deep learning neural networks to perform skin cancer detection.  ...  In this paper, the authors review the state-of-the-art in authoritative deep learning concepts pertinent to skin cancer detection and classification.  ...  Fig. 6 . 6 The architecture of a typical convolution neural network Fig. 7 . 7 (a) Traditional machine learning vs. (b) transfer learning Fig. 8 . 8 Sample of melanoma augmented images [57] .  ... 
doi:10.38094/jastt20189 fatcat:f7nansjvyrfxfmr27uzvp22oqi

Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

Muhammad Waqas Nadeem, Hock Guan Goh, Abid Ali, Muzammil Hussain, Muhammad Adnan Khan, Vasaki a/p Ponnusamy
2020 Diagnostics  
By considering a wide range of deep-learning applications, the main aim of this paper is to present a detailed survey on emerging research of deep-learning models for bone age assessment (e.g., segmentation  ...  Various applications, such as medical images analysis, medical images processing, text understanding, and speech recognition, have been using deep learning, and it has been providing rather promising results  ...  The convolutional neural network (CNN), deep belief network (DBN), and recurrent neural network (RNN) are powerful deep-learning models for image recognition, segmentation, prediction, and classification  ... 
doi:10.3390/diagnostics10100781 pmid:33022947 fatcat:k2bqi6crzjf3zlbpchehhjkwx4

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Muhammad Waqas Nadeem, Mohammed A. Al Ghamdi, Muzammil Hussain, Muhammad Adnan Khan, Khalid Masood Khan, Sultan H. Almotiri, Suhail Ashfaq Butt
2020 Brain Sciences  
In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics.  ...  A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/brainsci10020118 pmid:32098333 pmcid:PMC7071415 fatcat:wofq4puvcbemlconbz6carsf2y

Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art

Abubaker Abdelrahman, Serestina Viriri
2022 Journal of Imaging  
Deep learning (DL), particularly convolutional neural networks, has produced outstanding success in classifying and segmenting images.  ...  We discuss the various types of medical images and segmentation techniques and the assessment criteria for segmentation outcomes in kidney tumor segmentation, highlighting their building blocks and various  ...  Neural Networks Neural networks are a sort of learning algorithm that serves as the foundation for the majority of DL techniques [21, 27] .  ... 
doi:10.3390/jimaging8030055 pmid:35324610 pmcid:PMC8954467 fatcat:7dhh3zwk5zcmpe3ijzbgpmo4ze

Semantic Segmentation of Diabetic Foot Ulcer Images: Dealing with Small Dataset in DL Approaches [chapter]

Niri Rania, Hassan Douzi, Lucas Yves, Treuillet Sylvie
2020 Lecture Notes in Computer Science  
In this work, we performed DFU segmentation using Deep Learning methods for semantic segmentation. Our aim was to find an accurate fully convolutional neural network suitable to our small database.  ...  These preliminary results demonstrate the power of fully convolutional neural networks in diabetic foot ulcer segmentation using a limited number of training samples.  ...  This research work is supported by internal funding sources of PRISME laboratory, Orleans, France and from the European Union's Horizon 2020 under the Marie Sklodowska-Curie grant agreement No 777661.  ... 
doi:10.1007/978-3-030-51935-3_17 fatcat:zgebdganbveqxem6ltuxpt7znq

Region-based Convolution Neural Network Approach for Accurate Segmentation of Pelvic Radiograph [article]

Ata Jodeiri, Reza A. Zoroofi, Yuta Hiasa, Masaki Takao, Nobuhiko Sugano, Yoshinobu Sato, Yoshito Otake
2019 arXiv   pre-print
For training, the network weights were initialized by large non-medical dataset and fine-tuned with radiograph images.  ...  Segmentation of pelvic bone in radiograph images is a critical preprocessing step for some applications such as automatic pose estimation and disease detection.  ...  the Faster R-CNN [17] object detection framework with Fully Convolutional Networks (FCN) segmentation network [18] .  ... 
arXiv:1910.13231v2 fatcat:4y26ejoqszcwxogpretchwb4fi

Deep Learning: An Update for Radiologists

Phillip M. Cheng, Emmanuel Montagnon, Rikiya Yamashita, Ian Pan, Alexandre Cadrin-Chênevert, Francisco Perdigón Romero, Gabriel Chartrand, Samuel Kadoury, An Tang
2021 Radiographics  
Convolutional neural networks (CNNs), the core of deep learning methods for imaging, are multilayered artificial neural networks with weighted connections between neurons that are iteratively adjusted  ...  Familiarity with the key concepts described will help radiologists understand advances of deep learning in medical imaging and facilitate clinical adoption of these techniques.  ...  For image analysis, the fundamental architecture of deep learning systems is the convolutional neural network (CNN).  ... 
doi:10.1148/rg.2021200210 pmid:34469211 fatcat:rr3rxpmsbbd7th42w6hfiqc3uy

Target Detection and Classification of Brain Cancer Target Detection of Brain Cancer Using CNN

Mohak Jani, Keith Dsouza, Dr. Joanne Gomes, Nelson Dsouza
2022 Zenodo  
The patient, doctor or medical practitioners, paramedic etcetera are the users for the system. The proposed system acts like an assistant to the doctor, by detecting brain cancer in MRI images.  ...  Magnetic Resonance Imaging is the most effective technique for detecting brain tumours.  ...  A Convolutional Neural Network (CNN) is a form of Artificial Neural Network (ANN) that is unique. The development of CNN, a Deep learning algorithm, was influenced by ANNs.  ... 
doi:10.5281/zenodo.6324440 fatcat:jtwqwfjgrnbmxnookleuplij2m

Semantic segmentation of human oocyte images using deep neural networks

Anna Targosz, Piotr Przystałka, Ryszard Wiaderkiewicz, Grzegorz Mrugacz
2021 BioMedical Engineering OnLine  
This paper deals with semantic segmentation of human oocyte images using deep neural networks in order to develop new version of the predefined neural networks.  ...  Deep semantic oocyte segmentation networks can be seen as medically oriented predefined networks understanding the content of the image.  ...  networksFully convolutional neural networks • SegNet convolutional neural networks • U-Net convolutional neural networks Transfer learning technique was adopted due to the small number of learning  ... 
doi:10.1186/s12938-021-00864-w pmid:33892725 fatcat:d4lxtwnm3jby7hvp2x56bgiwp4

Segmentation of Human Brain Gliomas Tumour Images using U-Net Architecture with Transfer Learning

Assalah Zaki Alali, Khawla Hussein Ali
2022 Diyala Journal of Engineering Sciences  
The complexity of segmenting a brain tumour is critical in medical image processing. Treatment options and patient survival rates can only be improved if brain tumours can be prevented and treated.  ...  U-Nets with different transfer learning models as backbones are presented in this paper, there are ResNet50, DenseNet169 and EfficientNet-B7.  ...  [5] , residual neural network, dense convolutional network, and NASNet have been utilized in this study to build a fully programmed brain tumor recognition and segmentation, this deep learning architectures  ... 
doi:10.24237/djes.2022.15102 fatcat:d3n4drlx4nbxlew3hz2qf3sxf4

Medical image analysis based on deep learning approach

Muralikrishna Puttagunta, S. Ravi
2021 Multimedia tools and applications  
Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision.  ...  It provides a systematic review of the articles for classification, detection, and segmentation of medical images based on DLA.  ...  [68] proposed AlexNet is a deep convolutional neural network composed of 5 convolutional and 3 fully-connected layers.  ... 
doi:10.1007/s11042-021-10707-4 pmid:33841033 pmcid:PMC8023554 fatcat:cm522go4nbdbnglgzpw4nu7tbi

A 3D Convolutional Neural Network for Volumetric Image Semantic Segmentation

Hongya Lu, Haifeng Wang, Qianqian Zhang, Sang Won Yoon, Daehan Won
2019 Procedia Manufacturing  
This research proposes a novel 3D Convolutional Neural Network (CNN) to perform organ tissue segmentation from volumetric 3D medical images.  ...  Abstract This research proposes a novel 3D Convolutional Neural Network (CNN) to perform organ tissue segmentation from volumetric 3D medical images.  ...  At the last hidden layer, the sequences of signals will be input to a fully connected neural network to learn the features of the image, and generate the segmentation output as sequences of data.  ... 
doi:10.1016/j.promfg.2020.01.386 fatcat:s27622frzjfdrhhflj6ojiqkle
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