Evaluation of machine learning techniques for prostate cancer diagnosis and Gleason grading

Alexandratou, Eleni; Atlamazoglou, Vassilis; Thireou, Trias; Agrogiannis, George; Togas, Dimitrios; Kavantzas, Nikolaos; Patsouris, Efstratios; Yova, Dido

doi:10.1504/ijcibsb.2010.031392

Cited by 6 publications

(6 citation statements)

References 32 publications

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“…In this study, the ensemble framework was also compared to the typical previous CAD, which has been widely used in most of the previous studies [8], [9], [23]. However, the typical CAD had been focused on the study of the application of the single based solutions.…”

Section: B Empirical Results and Discussionmentioning

confidence: 99%

“…Texture-based CADs utilize the spatial distribution of the pixels in the tissue image to distinguish malignant from benign tissue or discerning the Gleason grades. For instance, the most conventional texture analysis for tissue image classification are co-occurrence matrices [20], [21] such as in [9], [22], [23] and fractal analysis [24], [25]. The Fractal analysis is used to describe the texture roughness at a specific location, analyze variations of intensity and texture complexity in tissue images [24], [25].…”

Section: Introductionmentioning

confidence: 99%

“…Alexandratou et al [23] compared between 16 machine learning algorithms based on their performance. In each algorithm, the training dataset extracted from the prostate tissue images based on thirteen Gray Level Co-occurrence Matrix (GLCM) to distinguish between the Gleason grades.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading

Albashish¹,

Sahran²,

Abdullah³

et al. 2016

International Journal on Advanced Science, Engineering and Information Technology

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Ensemble learning is an effective machine learning approach to improve the prediction performance by fusing several single classifier models. In computer-aided diagnosis system (CAD), machine learning has become one of the dominant solutions for tissue images diagnosis and grading. One problem in a single classifier model for multi-components of the tissue images combination to construct dense feature vectors is the overfitting. In this paper, an ensemble learning for multi-component tissue images classification approach is proposed. The prostate cancer Hematoxylin and Eosin (H&E) histopathology images from HUKM were used to test the proposed ensemble approach for diagnosing and Gleason grading. The experiments results of several prostate classification tasks, namely, benign vs. Grade 3, benign vs.Grade4, and Grade 3vs.Grade 4 show that the proposed ensemble significantly outperforms the previous typical CAD and the naïve approach that combines the texture features of all tissue component directly in dense feature vectors for a classifier.

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Section: B Empirical Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading

Albashish¹,

Sahran²,

Abdullah³

et al. 2016

International Journal on Advanced Science, Engineering and Information Technology

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“…There has been a wealth of work over the past twenty years to classify histological patterns in different disease sites and cancer types (e.g. Gleason Grade in prostate cancer, lung cancer, breast cancer, melanoma, lymphoma and neuroblastoma) using statistical methods and machine and deep learning techniques [ 154 ], [ 169 ], [ 170 ].…”

Section: Processing Analysis and Understanding In Digital Pathologymentioning

confidence: 99%

AI in Medical Imaging Informatics: Current Challenges and Future Directions

Panayides

Amini

Filipović

et al. 2020

IEEE J. Biomed. Health Inform.

292

119

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“…State-of-the-art CAD systems and methods for microscopy include a real-time decision support system for diagnosis of rare cancers [ 13 ]; a system for discrimination of normal from benign thyroid nodules in cytological images [ 14 ]; a system for detection and grading of carcinoma in histology images [ 15 ]; a method for prostate cancer diagnosis and grading [ 16 ]; a web-based software framework for segmentation of cervical cell nuclei in high-resolution microscopy images [ 17 ]; and a tool for classification of biological microscopic images of lung tissue sections with idiopathic pulmonary fibrosis [ 18 ]. These works indicate that texture plays an important role in the characterization of the content of microscopy images and that machine learning can be effective for automatic annotation of such images.…”

Section: Related Workmentioning

confidence: 99%

Ratsnake: A Versatile Image Annotation Tool with Application to Computer-Aided Diagnosis

Iakovidis

Γούδας

Smailis

et al. 2014

The Scientific World Journal

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Image segmentation and annotation are key components of image-based medical computer-aided diagnosis (CAD) systems. In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system. In order to demonstrate this unique capability, we present its novel application for the evaluation and quantification of salient objects and structures of interest in kidney biopsy images. Accurate annotation identifying and quantifying such structures in microscopy images can provide an estimation of pathogenesis in obstructive nephropathy, which is a rather common disease with severe implication in children and infants. However a tool for detecting and quantifying the disease is not yet available. A machine learning-based approach, which utilizes prior domain knowledge and textural image features, is considered for the generation of an image force field customizing the presented tool for automatic evaluation of kidney biopsy images. The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.

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Evaluation of machine learning techniques for prostate cancer diagnosis and Gleason grading

Cited by 6 publications

References 32 publications

Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading

Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading

AI in Medical Imaging Informatics: Current Challenges and Future Directions

Ratsnake: A Versatile Image Annotation Tool with Application to Computer-Aided Diagnosis

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