Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology

Janowczyk, Andrew; Basavanhally, Ajay; Madabhushi, Anant

doi:10.1016/j.compmedimag.2016.05.003

Cited by 175 publications

(115 citation statements)

References 21 publications

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“…Color normalization is an important research area in histopathology image analysis. In Janowczyk et al (2016a), a method for stain normalization of hematoxylin and eosin (H&E) stained histopathology images was presented based on deep sparse auto-encoders. Recently, the importance of color normalization was demonstrated by Sethi et al (2016) for CNN based tissue classification in H&E stained images.…”

Section: Digital Pathology and Microscopymentioning

confidence: 99%

See 1 more Smart Citation

A survey on deep learning in medical image analysis

Litjens

Kooi

Bejnordi

et al. 2017

Medical Image Analysis

9,175

5,414

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Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.

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Section: Digital Pathology and Microscopymentioning

confidence: 99%

“…Other image enhancement applications like intensity normalization and denoising have seen only limited application of deep learning algorithms. Janowczyk et al (2016a) used SAEs to normalize H&E-stained histopathology images whereas Benou et al (2016) used CNNs to perform denoising in DCE-MRI time-series.…”

Section: Image Generation and Enhancementmentioning

confidence: 99%

A survey on deep learning in medical image analysis

Litjens

Kooi

Bejnordi

et al. 2017

Medical Image Analysis

9,175

5,414

View full text Add to dashboard Cite

show abstract

“…[3] The high quality and consistency of whole slide images are becoming more important with an increase in the computer aided diagnostics. Recent works[456] propose methods to standardize color distribution of the whole slide images in a post-processing step. However, the workflow standardization is non-trivial mainly because it requires formalization of multiple processes, for example, specimen preparation, scanning, image postprocessing and displaying.…”

Section: Introductionmentioning

confidence: 99%

A quantitative approach to evaluate image quality of whole slide imaging scanners

Shrestha

Kneepkens

Vrijnsen

et al. 2016

Journal of Pathology Informatics

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Context:The quality of images produced by whole slide imaging (WSI) scanners has a direct influence on the readers’ performance and reliability of the clinical diagnosis. Therefore, WSI scanners should produce not only high quality but also consistent quality images.Aim:We aim to evaluate reproducibility of WSI scanners based on the quality of images produced over time and among multiple scanners. The evaluation is independent of content or context of test specimen.Methods:The ultimate judge of image quality is a pathologist, however, subjective evaluations are heavily influenced by the complexity of a case and subtle variations introduced by a scanner can be easily overlooked. Therefore, we employed a quantitative image quality assessment method based on clinically relevant parameters, such as sharpness and brightness, acquired in a survey of pathologists. The acceptable level of quality per parameter was determined in a subjective study. The evaluation of scanner reproducibility was conducted with Philips Ultra-Fast Scanners. A set of 36 HercepTest™ slides were used in three sub-studies addressing variations due to systems and time, producing 8640 test images for evaluation.Results:The results showed that the majority of images in all the sub-studies are within the acceptable quality level; however, some scanners produce higher quality images more often than others. The results are independent of case types, and they match our perception of quality.Conclusion:The quantitative image quality assessment method was successfully applied in the HercepTest™ slides to evaluate WSI scanner reproducibility. The proposed method is generic and applicable to any other types of slide stains and scanners.

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“…The features from lower layers of CNN model responds to the general attributes such as edge, color, texture and so on, while the higher layer features are more classspecific and abstract [18][19] [20]. Inspiring by the previous work which combined the handcrafted features and the features learned from deep CNN [15], this study proposed a sampler and efficient approach in which linked the output of conv3 and conv4 layer to fc6 to combine the extracted different level features.…”

Section: Multi-scale Fused Fully Convolutional Networkmentioning

confidence: 99%

Multi-Scale Deep Neural Network for Mitosis Detection in Breast Cancer Histological Images

Wu¹,

Fan²,

Qiao³

et al. 2017

Preprint

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Abstract. Accurate assessment of the breast cancer deterioration degree plays a crucial role in making medical plan, and the important basis for degree assessment is the number of mitoses in a given area of the pathological image. We utilized deep multi-scale fused fully convolutional neural network (MFF-CNN) combing with conditional random felid (CRF) to detect mitoses in hematoxylin and eosin stained histology image. Analyze the characteristics of mitotic detection ----scale invariance and sparsity, as well as the difficulties ----small amount of data , inconsistent image staining and sample class unbalanced. Based on this, mitotic detection model is designed. In this paper, a tissue-based staining equalization method is used, and to establish an effective training sample set, we select training samples by using CNN. A mitotic detection model fusing multi-level and multi-scale features and context information was designed, and the corresponding training strategy was made to reduce over-fitting. As preliminarily validated on the public 2014 ICPR MITOSIS data, our method achieves a better performance in term of detection accuracy than ever recorded for this dataset.

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Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology

Cited by 175 publications

References 21 publications

A survey on deep learning in medical image analysis

A survey on deep learning in medical image analysis

A quantitative approach to evaluate image quality of whole slide imaging scanners

Multi-Scale Deep Neural Network for Mitosis Detection in Breast Cancer Histological Images

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