Graph-based multi-resolution segmentation of histological whole slide images

Roullier, Vincent; Ta, V-T.; Lézoray, Olivier; Elmoataz, Abderrahim

doi:10.1109/isbi.2010.5490390

Cited by 10 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As it has been previously pointed out, a multi-resolution segmentation process is a natural approach to analyze whole slide images [7,9]. Indeed, we have seen in Sect.…”

Section: Principlementioning

confidence: 86%

Mitosis Extraction in Breast-Cancer Histopathological Whole Slide Images

Roullier

Lézoray

et al. 2010

Advances in Visual Computing

View full text Add to dashboard Cite

Abstract. In this paper, we present a graph-based multi-resolution approach for mitosis extraction in breast cancer histological whole slide images. The proposed segmentation uses a multi-resolution approach which reproduces the slide examination done by a pathologist. Each resolution level is analyzed with a focus of attention resulting from a coarser resolution level analysis. At each resolution level, a spatial refinement by semi-supervised clustering is performed to obtain more accurate segmentation around edges. The proposed segmentation is fully unsupervised by using domain specific knowledge.

show abstract

“…As it has been previously pointed out, a multi-resolution segmentation process is a natural approach to analyze whole slide images [7,9]. Indeed, we have seen in Sect.…”

Section: Principlementioning

confidence: 86%

Mitosis Extraction in Breast-Cancer Histopathological Whole Slide Images

Roullier

Lézoray

et al. 2010

Advances in Visual Computing

View full text Add to dashboard Cite

show abstract

“…Khan et al [ 9 ] proposed a statistical approach which modeled the intensity of pixels in mitotic and nonmitotic regions by a gamma-Gaussian mixture model that effectively detects mitosis in standard histology images. Roullier et al [ 10 ] presented a graph-based multiresolution approach for mitosis extraction in breast cancer histology images by segmentation at different resolutions based on a top-down approach. Fatakdawala et al [ 11 ] in their work used an expectation-maximization-driven contour technique with overlap for segmentation of lymphocytes in histology images.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images

Johny

2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

Diagnosis of different breast cancer stages using histopathology whole slide images (WSI) is the gold standard in determining the grade of tissue metastasis. Computer-aided diagnosis (CAD) assists medical experts as a second opinion tool in early detection to prevent further proliferation. The field of pathology has advanced so rapidly that it is possible to obtain high-quality images from glass slides. Patches from the region of interest in histopathology images are extracted and trained using artificial neural network models. The trained model primarily analyzes and predicts the histology images for the benign or malignant class to which it belongs. Classification of medical images focuses on the training of models with layers of abstraction to distinguish between these two classes with less false-positive rates. The learning rate is the crucial hyperparameter used during the training of deep convolutional neural networks (DCNN) to improve model accuracy. This work emphasizes the relevance of the dynamic learning rate than the fixed learning rate during the training of networks. The dynamic learning rate varies with preset conditions between the lower and upper boundaries and repeats at different iterations. The performance of the model thus improves and attains comparatively high accuracy with fewer iterations.

show abstract

“…Related Work. A variety of approaches have been developed to conduct automatic diagnosis based on pathological WSIs [8,9,10,11]. Due to the large size of the WSI, the direct use of the entire image as the input of the machine learning algorithms is impossible because of the great memory usage requirement [12].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the large size of the WSI, the direct use of the entire image as the input of the machine learning algorithms is impossible because of the great memory usage requirement [12]. Related solutions include, downsampling and region of interest (RoI) detection [8,9], multi-resolution analyzing [10], and extracting image patches [11,13,14,15].…”

Section: Introductionmentioning

confidence: 99%

Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet

Zhu

Mei

Peng

et al. 2020

Preprint

View full text Add to dashboard Cite

The automatic and objective medical diagnostic model can be valuable to achieve early cancer detection, and thus reducing the mortality rate. In this paper, we propose a highly efficient multi-level malignant tissue detection through the designed adversarial CAC-UNet. A patch-level model with a pre-prediction strategy and a malignancy area guided label smoothing is adopted to remove the negative WSIs, with which to lower the risk of false positive detection. For the selected key patches by multi-model ensemble, an adversarial context-aware and appearance consistency UNet (CAC-UNet) is designed to achieve robust segmentation. In CAC-UNet, mirror designed discriminators are able to seamlessly fuse the whole feature maps of the skillfully designed powerful backbone network without any information loss. Besides, a mask prior is further added to guide the accurate segmentation mask prediction through an extra mask-domain discriminator. The proposed scheme achieves the best results in MICCAI DigestPath2019 challenge 1 on colonoscopy tissue segmentation and classification task. The full implementation details and the trained models are available at https://github.com/Raykoooo/CAC-UNet.

show abstract

Graph-based multi-resolution segmentation of histological whole slide images

Cited by 10 publications

References 5 publications

Mitosis Extraction in Breast-Cancer Histopathological Whole Slide Images

Mitosis Extraction in Breast-Cancer Histopathological Whole Slide Images

Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images

Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet

Contact Info

Product

Resources

About