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
Study of Identification of Breast Tumor Through DL Technique
14 Nov 2022
TL;DR: In this paper , a deep learning approach is designed, developed, and evaluated on the entire slide pathology image database, which yielded an accuracy of 96.54% and outperformed the existing deep learning approaches since it automatically identifies the best available features.
•Posted Content
Fine-Tuning and Training of DenseNet for Histopathology Image Representation Using TCGA Diagnostic Slides
Abtin Riasatian,Morteza Babaie,Danial Maleki,Shivam Kalra,Mojtaba Valipour,Sobhan Hemati,Manit Zaveri,Amir Safarpoor,Sobhan Shafiei,Mehdi Afshari,Maral Rasoolijaberi,Milad Sikaroudi,Mohd Adnan,Sultaan Shah,Charles Choi,Savvas Damaskinos,Clinton J. V. Campbell,Phedias Diamandis,Liron Pantanowitz,Hany Kashani,Ali Ghodsi,Hamid R. Tizhoosh +21 more
TL;DR: KimiaNet as discussed by the authors employs the topology of the DenseNet with four dense blocks, fine-tuned and trained with histopathology images in different configurations, and uses high-cellularity mosaic approach to enable the usage of weak labels of 7,126 whole slide images of formalin-fixed paraffin-embedded human pathology samples publicly available through the Cancer Genome Atlas (TCGA) repository.
Breast Cancer Detection from Histopathology Images with Deep Inception and Residual Blocks
Singh Shiksha,Kumar Rajesh +1 more
TL;DR: This paper proposes a hybrid deep neural network combining Inception and Residual blocks for breast cancer detection from histopathology images, achieving high accuracy on two publicly available datasets, outperforming existing algorithms and conventional deep neural networks.
MF-OMKT: Model fusion based on online mutual knowledge transfer for breast cancer histopathological image classification
TL;DR: In this paper , a model fusion framework based on online mutual knowledge transfer (MF-OMKT) for breast cancer histopathological image classification is proposed, which can assist pathologists in improving breast cancer diagnosis accuracy and working efficiency.
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