Colorectal Cancer Outcome Prediction from H&amp;E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles

Yue, Xingzhi; Dimitriou, Neofytos; Arandjelović, Ognjen

doi:10.29007/n912

Cited by 19 publications

(42 citation statements)

References 25 publications

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“…It was then possible to train CNNs directly on the compressed WSIs (18). Others reduced the dimensionality of patches using traditional dimensionality reduction techniques, such as principal component analysis, as well as CNNs pretrained on ImageNet (16, 41). Both Zhu et al (41) and Yue et al (16) subsequently used k -means clustering and found the most discriminative clusters of patches by training CNNs in a weakly supervised manner (42).…”

Section: Deep Learningmentioning

confidence: 99%

“…Others reduced the dimensionality of patches using traditional dimensionality reduction techniques, such as principal component analysis, as well as CNNs pretrained on ImageNet (16, 41). Both Zhu et al (41) and Yue et al (16) subsequently used k -means clustering and found the most discriminative clusters of patches by training CNNs in a weakly supervised manner (42). However, the multi-stage structure of the aforementioned techniques does not allow processes that come first, i.e., patch compression or patch localization, to improve following the improvement of later processes, i.e., visual understanding.…”

Section: Deep Learningmentioning

confidence: 99%

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Deep Learning for Whole Slide Image Analysis: An Overview

2019

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The widespread adoption of whole slide imaging has increased the demand for effective and efficient gigapixel image analysis. Deep learning is at the forefront of computer vision, showcasing significant improvements over previous methodologies on visual understanding. However, whole slide images have billions of pixels and suffer from high morphological heterogeneity as well as from different types of artefacts. Collectively, these impede the conventional use of deep learning. For the clinical translation of deep learning solutions to become a reality, these challenges need to be addressed. In this paper, we review work on the interdisciplinary attempt of training deep neural networks using whole slide images, and highlight the different ideas underlying these methodologies.Recently, NHS Greater Glasgow and Clyde, one of the largest single site pathology services in Europe, has begun proceedings to undergo full digitization. As the adoption of digital pathology becomes wider, automated image analysis of tissue morphology has the potential to further establish itself in pathology and ultimately decrease the workload of pathologists, reduce turnaround times for reporting, and standardise clinical practices. For example, known or novel biomarkers and histopathological features can be automatically quantified [22,23,26,8,11,3,36,40,20]. Furthermore, deep learning techniques can be employed to recognize morphological patterns within the specimen for diagnostic and triaging purposes [38,48,32,50].

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Section: Deep Learningmentioning

confidence: 99%

Section: Deep Learningmentioning

confidence: 99%

Deep Learning for Whole Slide Image Analysis: An Overview

2019

Self Cite

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“…It is no longer an exaggeration to say that computer vision is pervasive in everyday life: Face detection [1,2] has been a standard feature of digital cameras and smartphones for well over a decade, online image depositories are increasingly successful at categorizing images by their semantic content (scene: Beach, city, countryside, etc; objects: Cars, buildings, dogs, churches, statues, etc.) [3,4], automatic diagnosis and prognosis of diseases has even surpassed the performance of human experts in some domains [5][6][7], etc. This success, coupled with the increasing pervasiveness of powerful computing devices and the dramatic improvement in user-friendliness of technology in general, is having a positive impact on inter-disciplinary research, with a growing interest in the application of modern computer science in other scientific fields, as well as in the arts and humanities [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Images of Roman Imperial Denarii: A Curated Data Set for the Evaluation of Computer Vision Algorithms Applied to Ancient Numismatics, and an Overview of Challenges in the Field

Arandjelović¹,

Zachariou²

2020

Sci

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Automatic ancient Roman coin analysis only recently emerged as a topic of computer science research. Nevertheless, owing to its ever-increasing popularity, the field is already reaching a certain degree of maturity, as witnessed by a substantial publication output in the last decade. At the same time, it is becoming evident that research progress is being limited by a somewhat veering direction of effort and the lack of a coherent framework which facilitates the acquisition and dissemination of robust, repeatable, and rigorous evidence. Thus, in the present article, we seek to address several associated challenges. To start with, (i) we provide a first overview and discussion of different challenges in the field, some of which have been scarcely investigated to date, and others which have hitherto been unrecognized and unaddressed. Secondly, (ii) we introduce the first data set, carefully curated and collected for the purpose of facilitating methodological evaluation of algorithms and, specifically, the effects of coin preservation grades on the performance of automatic methods. Indeed, until now, only one published work at all recognized the need for this kind of analysis, which, to any numismatist, would be a trivially obvious fact. We also discuss a wide range of considerations which had to be taken into account in collecting this corpus, explain our decisions, and describe its content in detail. Briefly, the data set comprises 100 different coin types, all with multiple examples in Fine, Very Fine, and Extremely Fine conditions, giving a total of over 650 different specimens. These correspond to 44 issuing authorities and span the time period of approximately 300 years (from 27 BC until 244 AD). In summary, the present article should be an invaluable resource to researchers in the field, and we encourage the community to adopt the collected corpus, freely available for research purposes, as a standard evaluation benchmark.

show abstract

“…It is no longer an exaggeration to say that computer vision is pervasive in everyday life: Face detection [1,2] has been a standard feature of digital cameras and smartphones for well over a decade, online image depositories are increasingly successful at categorizing images by their semantic content (scene: Beach, city, countryside, etc; objects: Cars, buildings, dogs, churches, statues, etc.) [3][4][5], automatic diagnosis and prognosis of diseases has even surpassed the performance of human experts in some domains [6][7][8], etc. This success, coupled with the increasing pervasiveness of powerful computing devices and the dramatic improvement in user-friendliness of technology in general, is having a positive impact on inter-disciplinary research, with a growing interest in the application of modern computer science in other scientific fields, as well as in the arts and humanities [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Images of Roman Imperial Denarii: A Curated Data Set for the Evaluation of Computer Vision Algorithms Applied to Ancient Numismatics, and an Overview of Challenges in the Field

Arandjelović¹,

Zachariou²

2020

Sci

View full text Add to dashboard Cite

Automatic ancient Roman coin analysis only recently emerged as a topic of computer science research. Nevertheless, owing to its ever-increasing popularity, the field is already reaching a certain degree of maturity, as witnessed by a substantial publication output in the last decade. At the same time, it is becoming evident that research progress is being limited by a somewhat veering direction of effort and the lack of a coherent framework which facilitates the acquisition and dissemination of robust, repeatable, and rigorous evidence. Thus, in the present article, we seek to address several associated challenges. To start with, (i) we provide a first overview and discussion of different challenges in the field, some of which have been scarcely investigated to date, and others which have hitherto been unrecognized and unaddressed. Secondly, (ii) we introduce the first data set, carefully curated and collected for the purpose of facilitating methodological evaluation of algorithms and, specifically, the effects of coin preservation grades on the performance of automatic methods. Indeed, until now, only one published work at all recognized the need for this kind of analysis, which, to any numismatist, would be a trivially obvious fact. We also discuss a wide range of considerations which had to be taken into account in collecting this corpus, explain our decisions, and describe its content in detail. Briefly, the data set comprises 100 different coin issues, all with multiple examples in Fine, Very Fine, and Extremely Fine conditions, giving a total of over 650 different specimens. These correspond to 44 issuing authorities and span the time period of approximately 300 years (from 27 BC until 244 AD). In summary, the present article should be an invaluable resource to researchers in the field, and we encourage the community to adopt the collected corpus, freely available for research purposes, as a standard evaluation benchmark.

show abstract

Colorectal Cancer Outcome Prediction from H&E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles

Cited by 19 publications

References 25 publications

Deep Learning for Whole Slide Image Analysis: An Overview

Deep Learning for Whole Slide Image Analysis: An Overview

Images of Roman Imperial Denarii: A Curated Data Set for the Evaluation of Computer Vision Algorithms Applied to Ancient Numismatics, and an Overview of Challenges in the Field

Images of Roman Imperial Denarii: A Curated Data Set for the Evaluation of Computer Vision Algorithms Applied to Ancient Numismatics, and an Overview of Challenges in the Field

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