A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes

Esteban, A. E.; López-Pérez, Miguel; Colomer, Adrián; Sales, María Á.; Molina, Rafael; Naranjo, Valery

doi:10.1016/j.cmpb.2019.07.003

Cited by 27 publications

(32 citation statements)

References 30 publications

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“…), table 4 shows the obtained results in the validation sets for the proposed network F SConv with different top models: fully-connected layers (FC), global-max pooling (GMP), global-average pooling (GAP), or a combination of them (GAP+FC or GMP+FC). Regarding the results obtained in the fine-tuned models, the use of architectures with residual blocks provided slightly worse results than the sequential approach, similarly as the previous results reported in the literature where sequential models used to outperform residual ones [17,19,20]. In relation to the use of different top models, no differences were found in the accuracy of the fine-tuned architectures, observing similar results for all of them.…”

Section: Fsconv Architecture Benchmarkingsupporting

confidence: 85%

“…A model trained using large databases of WSIs could be used for both WSIs and prostactetomies. The works in [12,13,14,15,16,17,18] follow the strategy of WSI analysis, while in [19,20,21] the authors use TMAs to develop the CAD models.…”

Section: Computer Vision Algorithms Have Been Widely Used To Analyse Histologicalmentioning

confidence: 99%

“…The database presented in this paper, SICAPv2, is, to the best of the authors's knowledge, the largest public collection of prostate H&E biopsies with local-level annotations of Gleason grades. SICAPv2 is an extension the database introduced in [17] and will be publicy available after the publication of this paper.…”

Section: Materials: Sicap Databasementioning

confidence: 99%

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Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection

Silva-Rodríguez

Colomer

Sales

et al. 2020

Computer Methods and Programs in Biomedicine

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Background and Objective:Prostate cancer is one of the most common diseases affecting men worldwide. The Gleason scoring system is the primary diagnostic and prognostic tool for prostate cancer. Furthermore, recent reports indicate that the presence of patterns of the Gleason scale such as the cribriform pattern may also correlate with a worse prognosis compared to other patterns belonging to the Gleason grade 4. Current clinical guidelines have indicated the convenience of highlight its presence during the analysis of biopsies. All these requirements suppose a great

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Section: Fsconv Architecture Benchmarkingsupporting

confidence: 85%

Section: Computer Vision Algorithms Have Been Widely Used To Analyse Histologicalmentioning

confidence: 99%

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Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection

Silva-Rodríguez

Colomer

Sales

et al. 2020

Computer Methods and Programs in Biomedicine

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“…For these sets, patches of size 512 2 pixels are extracted at 10× resolution. This choice is motivated by prior literature, which determined this configuration as the most optimum for the binary cancer vs. no cancerous supervised classification task [35]. Furthermore, the main study on supervised learning used for comparison, i.e., [26], employs the same patch size, which makes direct comparison easier.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Self-Learning for Weakly Supervised Gleason Grading of Local Patterns

Silva-Rodríguez

Colomer

Dolz

et al. 2021

IEEE J. Biomed. Health Inform.

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Prostate cancer is one of the main diseases affecting men worldwide. The gold standard for diagnosis and prognosis is the Gleason grading system. In this process, pathologists manually analyze prostate histology slides under microscope, in a high time-consuming and subjective task. In the last years, computeraided-diagnosis (CAD) systems have emerged as a promising tool that could support pathologists in the daily clinical practice. Nevertheless, these systems are usually trained using tedious and prone-to-error pixel-level annotations of Gleason grades in the tissue. To alleviate the need of manual pixel-wise labeling, just a handful of works have been presented in the literature. Furthermore, despite the promising results achieved on global scoring the location of cancerous patterns in the tissue is only qualitatively addressed. These heatmaps of tumor regions, however, are crucial to the reliability of CAD systems as they provide explainability to the system's output and give confidence to pathologists that the model is focusing on medical relevant features. Motivated by this, we propose a novel weakly-supervised deep-learning model, based on self-learning CNNs, that leverages only the global Gleason score of gigapixel whole slide images during training to accurately perform both, grading of patch-level patterns and biopsy-level scoring. To evaluate the performance of the proposed method, we perform extensive experiments on three different external datasets for the patch-level Gleason grading, and on two different test sets for global Grade Group prediction. We empirically demonstrate that our approach outperforms its supervised counterpart on patchlevel Gleason grading by a large margin, as well as state-of-the-art methods on global biopsy-level scoring. Particularly, the proposed model brings an average improvement on the Cohen's quadratic kappa (κ) score of nearly 18% compared to full-supervision for the patch-level Gleason grading task. This suggests that the absence of the annotator's bias in our approach and the capability of using large weakly labeled datasets during training leads to higher performing and more robust models. Furthermore, raw features obtained from the patch-level classifier showed to generalize better than previous approaches in the literature to the subjective global biopsy-level scoring.

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“…In Karimi et al [8] they proposed training three CNNs for patches of different sizes, and summarizing the probabilities by a logistic regression. In [4], the authors use Gaussian processes based on granulometry descriptors extracted with a CNN for the binary classification task. Some other CNN architectures for GS grading include a combination of an atrous spatial pyramid pooling and a regular CNN as in [11], an Inception-v3 CNN with a support vector machine (SVM) as in [12], and a DeepLabV3+ with a MobileNet as the backbone [9].…”

Section: Related Workmentioning

confidence: 99%

Prostate Tissue Grading with Deep Quantum Measurement Ordinal Regression

Toledo-Cortés¹,

Useche²,

González³

2021

Preprint

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Prostate cancer (PCa) is one of the most common and aggressive cancers worldwide. The Gleason score (GS) system is the standard way of classifying prostate cancer and the most reliable method to determine the severity and treatment to follow. The pathologist looks at the arrangement of cancer cells in the prostate and assigns a score on a scale that ranges from 6 to 10. Automatic analysis of prostate wholeslide images (WSIs) is usually addressed as a binary classification problem, which misses the finer distinction between stages given by the GS. This paper presents a probabilistic deep learning ordinal classification method that can estimate the GS from a prostate WSI. Approaching the problem as an ordinal regression task using a differentiable probabilistic model not only improves the interpretability of the results, but also improves the accuracy of the model when compared to conventional deep classification and regression architectures.

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A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes

Cited by 27 publications

References 30 publications

Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection

Going deeper through the Gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection

Self-Learning for Weakly Supervised Gleason Grading of Local Patterns

Prostate Tissue Grading with Deep Quantum Measurement Ordinal Regression

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