Proceedings Article10.1109/SMC.2017.8122889
Deep features for breast cancer histopathological image classification
Fabio Alexandre Spanhol,Luiz S. Oliveira,Paulo R. Cavalin,Caroline Petitjean,Laurent Heutte +4 more
- 01 Oct 2017
- pp 1868-1873
337
TL;DR: The experimental evaluation of DeCaf features for BC recognition shows that these features can be a viable alternative to fast development of high-accuracy BC recognition systems, generally achieving better results than traditional hand-crafted textural descriptors and outperforming task-specific CNNs in some cases.
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Abstract: Breast cancer (BC) is a deadly disease, killing millions of people every year. Developing automated malignant BC detection system applied on patient's imagery can help dealing with this problem more efficiently, making diagnosis more scalable and less prone to errors. Not less importantly, such kind of research can be extended to other types of cancer, making even more impact to help saving lives. Recent results on BC recognition show that Convolution Neural Networks (CNN) can achieve higher recognition rates than hand-crafted feature descriptors, but the price to pay is an increase in complexity to develop the system, requiring longer training time and specific expertise to fine-tune the architecture of the CNN. DeCAF (or deep) features consist of an in-between solution it is based on reusing a previously trained CNN only as feature vectors, which is then used as input for a classifier trained only for the new classification task. In the light of this, we present an evaluation of DeCaf features for BC recognition, in order to better understand how they compare to the other approaches. The experimental evaluation shows that these features can be a viable alternative to fast development of high-accuracy BC recognition systems, generally achieving better results than traditional hand-crafted textural descriptors and outperforming task-specific CNNs in some cases.
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Citations
Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions.
Ahsan Bin Tufail,Ahsan Bin Tufail,Yong-Kui Ma,Mohammed K. A. Kaabar,Francisco Martínez,A R Junejo,Inam Ullah,Rahim Khan +7 more
TL;DR: Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design as mentioned in this paper, which has emerged as a technology of choice due to the availability of high computational resources.
Advances in Deep Learning-Based Medical Image Analysis
Xiaoqing Liu,Gao Kunlun,Bo Liu,Chengwei Pan,Kongming Liang,Yan Lifeng,Jiechao Ma,He Fujin,Shu Zhang,Siyuan Pan,Yizhou Yu +10 more
- 16 Jun 2021
TL;DR: Overall, according to the best available evidence, deep learning models performed well in medical image analysis, but what cannot be ignored are the algorithms derived from small-scale medical datasets impeding the clinical applicability.
Computer-Aided Histopathological Image Analysis Techniques for Automated Nuclear Atypia Scoring of Breast Cancer: a Review
TL;DR: The various algorithms applied for the nuclear pleomorphism scoring of breast cancer are discussed, the challenges to be dealt with, and the importance of benchmark datasets are outlined.
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Classification of Breast Cancer Histopathological Images Using Discriminative Patches Screened by Generative Adversarial Networks
Rui Man,Ping Yang,Bowen Xu +2 more
TL;DR: A novel approach, named DenseNet121-AnoGAN, for classifying breast histopathological images into benign and malignant classes is proposed, which can be better suited to coarse-grained high-resolution images and achieved satisfactory classification performance in 40X and 100X images.
References
•Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton +2 more
- 03 Dec 2012
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
ImageNet: A large-scale hierarchical image database
Jia Deng,Wei Dong,Richard Socher,Li-Jia Li,Kai Li,Li Fei-Fei +5 more
- 20 Jun 2009
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
Representation Learning: A Review and New Perspectives
TL;DR: Recent work in the area of unsupervised feature learning and deep learning is reviewed, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks.
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Hoo-Chang Shin,Holger R. Roth,Mingchen Gao,Le Lu,Ziyue Xu,Isabella Nogues,Jianhua Yao,Daniel J. Mollura,Ronald M. Summers +8 more
TL;DR: Two specific computer-aided detection problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification are studied, achieving the state-of-the-art performance on the mediastinal LN detection, and the first five-fold cross-validation classification results are reported.
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