Hybrid Unified Deep Learning Network for Highly Precise Gleason Grading of Prostate Cancer

Poojitha, Uthappa P.; Sharma, Shanker Lal

doi:10.1109/embc.2019.8856912

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…In addition to classification and explainability, semantic segmentation approaches can also be applied on histopathology images to localise specific regions. One notable approach to perform semantic segmentation is to use generative adversarial networks (GANs) [ 47 ]. GAN is a versatile generative DL method comprising a pair of two neural networks: a generator and a discriminator [ 83 ].…”

Section: Deep Learning In Oncologymentioning

confidence: 99%

“…This ‘adversarial’ mechanism forces the generator to be as accurate as possible in localising objects so that the discriminator cannot recognise the difference between predicted and ground-truth class labels [ 84 ]. Using this approach, Poojitha and Lal Sharma trained a CNN-based generator to segment cancer tissue to ‘help’ a CNN-based classifier predict prostate cancer grading [ 47 ]. The GAN-annotated tissue maps helped the CNN classifier achieve comparable accuracy to the grading produced by anatomical pathologists, indicating DL models can detect relevant cell regions in pathology images for decision making.…”

Section: Deep Learning In Oncologymentioning

confidence: 99%

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Deep learning in cancer diagnosis, prognosis and treatment selection

et al. 2021

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Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer. While early results are promising, this is a rapidly evolving field with new knowledge emerging in both cancer biology and deep learning. In this review, we provide an overview of emerging deep learning techniques and how they are being applied to oncology. We focus on the deep learning applications for omics data types, including genomic, methylation and transcriptomic data, as well as histopathology-based genomic inference, and provide perspectives on how the different data types can be integrated to develop decision support tools. We provide specific examples of how deep learning may be applied in cancer diagnosis, prognosis and treatment management. We also assess the current limitations and challenges for the application of deep learning in precision oncology, including the lack of phenotypically rich data and the need for more explainable deep learning models. Finally, we conclude with a discussion of how current obstacles can be overcome to enable future clinical utilisation of deep learning.

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Section: Deep Learning In Oncologymentioning

confidence: 99%

Section: Deep Learning In Oncologymentioning

confidence: 99%

Deep learning in cancer diagnosis, prognosis and treatment selection

et al. 2021

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“…When estimating Gleason grading, many papers only focused on classifying tiles or small regions like TMAs by taking advantage of classical CNN architectures trained on large datasets of natural images such as ImageNet [ 60 ]. In that context, tiles were encoded into features which corresponded to the input data for classification [ 21 , 38 , 61 , 62 , 63 , 64 , 65 , 66 ]. One of the first papers in the field used a cohort of 641 TMAs, obtaining a quadratic Cohen Kappa of 0.71 [ 21 ].…”

Section: Resultsmentioning

confidence: 99%

Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review

et al. 2023

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Deep learning (DL), often called artificial intelligence (AI), has been increasingly used in Pathology thanks to the use of scanners to digitize slides which allow us to visualize them on monitors and process them with AI algorithms. Many articles have focused on DL applied to prostate cancer (PCa). This systematic review explains the DL applications and their performances for PCa in digital pathology. Article research was performed using PubMed and Embase to collect relevant articles. A Risk of Bias (RoB) was assessed with an adaptation of the QUADAS-2 tool. Out of the 77 included studies, eight focused on pre-processing tasks such as quality assessment or staining normalization. Most articles (n = 53) focused on diagnosis tasks like cancer detection or Gleason grading. Fifteen articles focused on prediction tasks, such as recurrence prediction or genomic correlations. Best performances were reached for cancer detection with an Area Under the Curve (AUC) up to 0.99 with algorithms already available for routine diagnosis. A few biases outlined by the RoB analysis are often found in these articles, such as the lack of external validation. This review was registered on PROSPERO under CRD42023418661.

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Section: Grade and Let Gradementioning

confidence: 99%

“…Alternatively, some approaches were only trained to identify malignant regions [20,21]. Novel work has also been presented regarding new methodological approaches of automatic Gleason grading [22,23] Lastly, it deserves attention that not only routine stains can be more efficiently analyzed by artificial intelligence: Harmon et al [24] have presented an algorithm that segments PTEN IHC-stained tissue and can make an accurate prediction of tumor-specific PTEN loss with 90% sensitivity and 95% specificity.…”

Section: An Illustrative Study By Egevad Et Al [8mentioning

confidence: 99%

Artificial intelligence in prostate histopathology: where are we in 2021?

Oszwald¹,

Wasinger²,

Pradère

et al. 2021

Current Opinion in Urology

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Purpose of review Artificial intelligence has made an entrance into mainstream applications of daily life but the clinical deployment of artificial intelligence-supported histological analysis is still at infancy. Recent years have seen a surge in technological advance regarding the use of artificial intelligence in pathology, in particular in the diagnosis of prostate cancer. Recent findings We review first impressions of how artificial intelligence impacts the clinical performance of pathologists in the analysis of prostate tissue. Several challenges in the deployment of artificial intelligence remain to be overcome. Finally, we discuss how artificial intelligence can help in generating new knowledge that is interpretable by humans. Summary It is evident that artificial intelligence has the potential to outperform most pathologists in detecting prostate cancer, and does not suffer from inherent interobserver variability. Nonetheless, large clinical validation studies that unequivocally prove the benefit of artificial intelligence support in pathology are necessary. Regardless, artificial intelligence may soon automate and standardize many facets of routine work, including qualitative (i.e. Gleason Grading) and quantitative measures (i.e. portion of Gleason Grades and tumor volume). For the near future, a model where pathologists are enhanced by second-review or real-time artificial intelligence systems appears to be the most promising approach.

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Hybrid Unified Deep Learning Network for Highly Precise Gleason Grading of Prostate Cancer

Cited by 13 publications

References 8 publications

Deep learning in cancer diagnosis, prognosis and treatment selection

Deep learning in cancer diagnosis, prognosis and treatment selection

Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review

Artificial intelligence in prostate histopathology: where are we in 2021?

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