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Deep neural network models for computational histopathology: A survey [article]

Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel
2019 arXiv   pre-print
We also provide an overview of deep learning based survival models that are applicable for disease-specific prognosis tasks.  ...  In this paper, we present a comprehensive review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis.  ...  Conclusions In this survey, we have presented a comprehensive overview of deep neural network models developed in the context of computational histopathology image analysis.  ... 
arXiv:1912.12378v1 fatcat:xdfkzzwzb5alhjfhffqpcurb2u

A Survey on Graph-Based Deep Learning for Computational Histopathology [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
However, learning over patch-wise features using convolutional neural networks limits the ability of the model to capture global contextual information and comprehensively model tissue composition.  ...  With the remarkable success of representation learning for prediction problems, we have witnessed a rapid expansion of the use of machine learning and deep learning for the analysis of digital pathology  ...  Our survey covers a new rapidly growing field of representation learning for computational histopathology.  ... 
arXiv:2107.00272v2 fatcat:3eskkeref5ccniqsjgo3hqv2sa

A Systematic Survey on Automatic Classification of Breast Cancer using Histopathology Image

Shwetha G.K
2020 Bioscience Biotechnology Research Communications  
In order to highlight these issues, several models are developed by the researchers for automatic classification of breast cancer.  ...  KEY WORDS: BreaST cancer claSSIFIcaTIon, Deep learnIng TechnIqueS, hISTopaThology ImageS, Image DenoISIng, machIne learnIng TechnIqueS.  ...  Some examples of unsupervised learning category are Deep neural network (Dnn), auto encoder, convolutional neural network (cnn), graph neural network, capsule network architecture, etc.  ... 
doi:10.21786/bbrc/13.13/54 fatcat:fhcbqtgr4jd7zaqppebrrw24fq

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

Sarah M. Ayyad, Mohamed Shehata, Ahmed Shalaby, Mohamed Abou El-Ghar, Mohammed Ghazal, Moumen El-Melegy, Nahla B. Abdel-Hamid, Labib M. Labib, H. Arafat Ali, Ayman El-Baz
2021 Sensors  
However, there is a lack of comprehensive surveys that focus on prostate cancer using histopathology images.  ...  is presented specifically for histopathology images tailored for prostate cancer.  ...  In the last decade, neural network architectures like convolution neural network (CNN), fully convolutional network (FCN), deep neural networks (DNN), and generative adversarial networks (GAN) are attracting  ... 
doi:10.3390/s21082586 pmid:33917035 fatcat:qfspvswivrbnlaih5y4gun5zwm

Breast Cancer Detection using Histopathological Images [article]

Jitendra Maan, Harsh Maan
2022 arXiv   pre-print
Therefore, we propose a saliency detection system with the help of advanced deep learning techniques, such that the machine will be taught to emulate actions of pathologists for localization of diagnostically  ...  We study identification of five diagnostic categories of breast cancer by training a CNN (VGG16, ResNet architecture). We have used BreakHis dataset to train our model.  ...  We have implemented Max-Pooling operation/layer in our CNNbased model. Huge number of neurons are present in a deep neural network which allows the network to make large number of predictions.  ... 
arXiv:2202.06109v1 fatcat:p7lrinugrnc7zduxqpwjwjyeva

Model Fooling Attacks Against Medical Imaging: A Short Survey

Tuomo Sipola, Samir Puuska, Tero Kokkonen
2020 Information & Security An International Journal  
Acknowledgements This research is partially funded by the Cyber Security Network of Competence Centres for Europe (CyberSec4Europe) project of the Horizon 2020 SU-ICT-03-2018 program.  ...  This paper presents a short survey of model fooling attacks against neural networks in the medical domain.  ...  See 3 for a survey of adversarial attacks against deep learning in computer vision. The authors not only list several attacks but also include defences.  ... 
doi:10.11610/isij.4615 fatcat:vg5xo6wiwfgk5pnm66d2bfgi5u

A Survey on Image Retrieval Techniques [chapter]

Lalitha K, Murugavalli S
2020 Advances in Parallel Computing  
This paper is presented with the survey of different Image retrieval techniques which used various techniques from visual features to the latest deep learning with Convolutional Neural Network(CNN) which  ...  become the best approach for image retrieval with number of layers applicable for large database.  ...  The deep features of the image are extracted using a pretrained g) deep Convolutional Neural Network model. Lattice-based homomorphic method concealed the information about neural network.  ... 
doi:10.3233/apc200174 fatcat:dndecfaujba4plwus45mbiyyre

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

Muhammad Firoz Mridha, Md. Abdul Hamid, Muhammad Mostafa Monowar, Ashfia Jannat Keya, Abu Quwsar Ohi, Md. Rashedul Islam, Jong-Myon Kim
2021 Cancers  
This review focuses on the evolving architectures of deep learning for breast cancer detection.  ...  Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.  ...  Additionally, special thanks are given to the Advanced Machine Learning lab, BUBT and the Computer Vision & Pattern Recognition Lab, UAP for providing facilities in which to research and publish.  ... 
doi:10.3390/cancers13236116 pmid:34885225 fatcat:ircywikuuvc25laiz3fsrc65bq

A Survey of Deep Active Learning [article]

Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B. Gupta, Xiaojiang Chen, Xin Wang
2021 arXiv   pre-print
Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters, so that the model learns how to extract high-quality features.  ...  Although the related research has been quite abundant, it lacks a comprehensive survey of DAL.  ...  (a) A common DL model: Convolutional Neural Network.  ... 
arXiv:2009.00236v2 fatcat:zuk2doushzhlfaufcyhoktxj7e

A Survey on Deep Learning for Precision Oncology

Ching-Wei Wang, Muhammad-Adil Khalil, Nabila Puspita Firdi
2022 Diagnostics  
First, we survey the deep-learning approaches categorized by various precision oncology tasks, including the estimation of dose distribution for treatment planning, survival analysis and risk estimation  ...  Deep learning has become the main method for precision oncology.  ...  (a) Convolution Neural Network (CNN), (b) Recurrent Neural Network (RNN), (c) Deep Neural Network (DNN), and (d) Generative Adversarial Network (GAN). Figure 2 . 2 Figure 2.  ... 
doi:10.3390/diagnostics12061489 pmid:35741298 pmcid:PMC9222056 fatcat:2qgvdz4x7rejxkwgoascxk77ke

HISTOPATHOLOGY IMAGE CLASSIFICATION USING HYBRID PARALLEL STRUCTURED DEEP-CNN MODELS

Kevin Joy DSOUZA, Zahid Ahmed ANSARI
2022 Applied Computer Science  
Further, the proposed models are parallelized using the TensorFlow-GPU framework to accelerate the training of these deep CNN (Convolution Neural Networks) architectures.  ...  Hence this study presents a hybrid deep learning architecture to classify the histopathology images to identify the presence of cancer in a patient.  ...  Convolutional neural networks are a class of feed-forward neural networks predominantly used for image processing.  ... 
doi:10.23743/acs-2022-02 doaj:085b1248ad784cd8bc9b3d935efec684 fatcat:zturxygzazggxgtbva25qczn3y

Efficient High-Resolution Deep Learning: A Survey [article]

Arian Bakhtiarnia, Qi Zhang, Alexandros Iosifidis
2022 arXiv   pre-print
Using high-resolution images and videos as direct inputs for deep learning models creates many challenges due to their high number of parameters, computation cost, inference latency and GPU memory consumption  ...  Such high-resolution data often need to be processed by deep learning models for cancer detection, automated road navigation, weather prediction, surveillance, optimizing agricultural processes and many  ...  For instance, a survey on deep learning for histopathology, which mentions challenges with processing the giga-resolution of WSIs, is provided in [20] ; a survey on crowd counting where many of the available  ... 
arXiv:2207.13050v1 fatcat:bcmi2kcf55gzve44w6cpez5dvm

Neuroplastic graph attention networks for nuclei segmentation in histopathology images [article]

Yoav Alon, Huiyu Zhou
2022 arXiv   pre-print
In experimental evaluation, our framework outperforms ensembles of state-of-the-art neural networks, with a fraction of the neurons typically required, and sets new standards for the segmentation of new  ...  State-of-the-art deep learning approaches predominantly apply convolutional layers in segmentation and are typically highly customized for a specific experimental configuration; often unable to generalize  ...  A comprehensive survey of existing approaches for Graph Neural Networks and their application can be found in Wu et al. (2019) ; Zhang et al. (2018) .  ... 
arXiv:2201.03669v1 fatcat:uujxbsnmlfbfbagnm5umjjejka

Transfer learning for cancer diagnosis in histopathological images

Sandhya Aneja, Nagender Aneja, Pg Emeroylariffion Abas, Abdul Ghani Naim
2022 IAES International Journal of Artificial Intelligence (IJ-AI)  
In modern computer vision research, the question is which architecture performs better for a given dataset.  ...  In this paper, we compare the performance of 14 pre-trained ImageNet models on the histopathologic cancer detection dataset, where each model has been configured as naive model, feature extractor model  ...  A union deep neural network is trained on modified source and target domain. c.  ... 
doi:10.11591/ijai.v11.i1.pp129-136 fatcat:rdjuerhmp5gb7khf25emeij6ne

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks [article]

Xiaomin Zhou, Chen Li, Md Mamunur Rahaman, Yudong Yao, Shiliang Ai, Changhao Sun, Xiaoyan Li, Qian Wang, Tao Jiang
2020 arXiv   pre-print
First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented.  ...  To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast  ...  Guoxian Li for their important discussion.  ... 
arXiv:2003.12255v2 fatcat:dghl3hszhrb7zlidym5z2x3mvq
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