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
A self-learning deep neural network for classification of breast histopathological images
TL;DR: This study proposes a self-learning deep neural network for breast cancer classification using histopathological images, achieving 99.1% accuracy with a hierarchical label correction method, improving accuracy, efficiency, and interpretability of classification results.
14
Towards Interactive Breast Tumor Classification Using Transfer Learning
Nick Weiss,Henning Kost,André Homeyer +2 more
- 27 Jun 2018
TL;DR: A classification method is presented that enables fast training with a limited number of samples and achieves state-of-the-art results on the diagnosis of breast cancer.
14
Accuracy Improvement in Detection of COVID-19 in Chest Radiography
Yasin Yari,Thuy Van Nguyen,Hieu T. Nguyen +2 more
- 14 Dec 2020
TL;DR: Li et al. as mentioned in this paper proposed an effective deep transfer learning-based model that improves current state-of-the-art systems in COVID-19 detection in chest radiographs, and the weights of the DesneNet121 and ResNet50 on the Imagenet have transferred as initial weights, and then the two models were fine-tuned with a deep classifier with data augmentation to detect three classes of COVID19, Viral Pneumonia and normal radiographs.
FabNet: A Features Agglomeration-Based Convolutional Neural Network for Multiscale Breast Cancer Histopathology Images Classification
Muhammad Sadiq Amin,Hyun Sik Ahn +1 more
TL;DR: In this paper , a CNN model architecture for cancer image classification is proposed, which combines the feature hierarchy in an iterative and hierarchically manner that infers higher accuracy and fewer parameters.
Encoder-Decoder Based CNN Structure for Microscopic Image Identification
Dawid Połap,Marcin Wozniak,Marcin Korytkowski,Rafal Scherer +3 more
- 18 Nov 2020
TL;DR: This research proposes a novel solution based on convolutional auto-encoders and additional two-dimensional image processing techniques to achieve better efficiency in the detection and classification of small objects in images obtained from various microscopes.
13
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