Multiple instance learning for histopathological breast cancer image classification

Sudharshan, P. J.; Petitjean, Caroline; Spanhol, Fábio Alexandre; Oliveira, Luiz S.; Heutte, Laurent; Honeiné, Paul

doi:10.1016/j.eswa.2018.09.049

Cited by 313 publications

(198 citation statements)

References 15 publications

Supporting

Mentioning

196

Contrasting

Unclassified

Order By: Relevance

“…Digital Pathology has grown considerably in recent years encompassing computerbased activities that allow for improvements and innovations in the workflow of pathology [1]. In this domain the automated processing of tissue samples has received increasing attention due to the potential applications in diagnosis [2], grading [3], identification of tissue substructures [4], prognostication and mutation prediction [5]. A number of problems, however, still limit the adoption of digital pathology on a large scale: the relatively scarce availability of large labelled datasets of histological images, the differences in the acquisition systems and/or protocol used as well as the variability in tissue preparation and/or stain reactivity [6].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images

Bianconi

Kather

Reyes-Aldasoro

2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

This paper compares the effects of colour pre-processing on the classification performance of H&E-stained images. Variations in the tissue preparation procedures, acquisition systems, stain conditions and reagents are all source of artifacts that can affect negatively computerbased classification. Pre-processing methods such as colour constancy, transfer and deconvolution have been proposed to compensate the artifacts. In this paper we compare quantitatively the combined effect of six colour pre-processing procedures and 12 colour texture descriptors on patch-based classification of H&E-stained images. We found that colour pre-processing had negative effects on accuracy in most cases -particularly when used with colour descriptors. However, some pre-processing procedures proved beneficial when employed in conjunction with classic texture descriptors such as co-occurrence matrices, Gabor filters and Local Binary Patterns.

show abstract

Section: Introductionmentioning

confidence: 99%

Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images

Bianconi

Kather

Reyes-Aldasoro

2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Besides, this problem can be seen as a multiple instance learning (MIL) problem [25] and that a MILbased approach can be a promising path for future work.…”

Section: Resultsmentioning

confidence: 99%

Efficient Deep Learning Model for COVID-19 Detection in large CT images datasets: A cross-dataset analysis

Luz

Silva

Moreira

et al. 2020

Preprint

View full text Add to dashboard Cite

Early detection and diagnosis are critical factors to control the COVID-19 spreading. A number of deep learning-based methodologies have been recently proposed for COVID-19 screening in CT scans as a tool to automate and help with the diagnosis. To achieve these goals, in this work, we propose a slice voting-based approach extending the EfficientNet Family of deep artificial neural networks.We also design a specific data augmentation process and transfer learning for such task.Moreover, a cross-dataset study is performed into the two largest datasets to date. The proposed method presents comparable results to the state-of-the-art methods and the highest accuracy to date on both datasets (accuracy of 87.60\% for the COVID-CT dataset and accuracy of 98.99% for the SARS-CoV-2 CT-scan dataset). The cross-dataset analysis showed that the generalization power of deep learning models is far from acceptable for the task since accuracy drops from 87.68% to 56.16% on the best evaluation scenario.These results highlighted that the methods that aim at COVID-19 detection in CT-images have to improve significantly to be considered as a clinical option and larger and more diverse datasets are needed to evaluate the methods in a realistic scenario.

show abstract

“…Surprisingly, we achieved 85.1% of accuracy using the TCNN without data augmentation, a performance comparable to the baseline which employs an AlexNet CNN with millions of trainable parameters. Approach Accuracy (%) CNN (Alexnet) [7] 84.6 TCNN (DA 1×) 85.1 Baseline [4] 85.1 TCNN Inc (DA 72×) 85.7 Deep Features (DeCaf) [12] 86.3 CNN+Fisher [13] 86.9 MI Approach [11] 87.2 Inception V3 FT (DA 72×) 87.4…”

Section: Resultsmentioning

confidence: 99%

Texture CNN for Histopathological Image Classification

Matos¹,

Britto

Oliveira

et al. 2019

2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)

View full text Add to dashboard Cite

Biopsies are the gold standard for breast cancer diagnosis. This task can be improved by the use of Computer Aided Diagnosis (CAD) systems, reducing the time of diagnosis and reducing the inter and intra-observer variability. The advances in computing have brought this type of system closer to reality. However, datasets of Histopathological Images (HI) from biopsies are quite small and unbalanced what makes difficult to use modern machine learning techniques such as deep learning. In this paper we propose a compact architecture based on texture filters that has fewer parameters than traditional deep models but is able to capture the difference between malignant and benign tissues with relative accuracy. The experimental results on the BreakHis dataset have show that the proposed texture CNN achieves almost 90% of accuracy for classifying benign and malignant tissues.

show abstract

Multiple instance learning for histopathological breast cancer image classification

Cited by 313 publications

References 15 publications

Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images

Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images

Efficient Deep Learning Model for COVID-19 Detection in large CT images datasets: A cross-dataset analysis

Texture CNN for Histopathological Image Classification

Contact Info

Product

Resources

About