Clustering Analysis for Semi-supervised Learning Improves Classification Performance of Digital Pathology

Peikari, Mohammad; Zubovits, Judit; Clarke, Geoff; Martel, Anne L.

doi:10.1007/978-3-319-24888-2_32

Cited by 12 publications

(9 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We chose to use an SVM classifier as we found that it performed well on related tasks . We did explore the use of a random forest classifier on a small subset of our nuclei figure classification dataset and found that the performance of the SVM was better.…”

Section: Discussionmentioning

confidence: 99%

Automatic cellularity assessment from post‐treated breast surgical specimens

et al. 2017

Self Cite

View full text Add to dashboard Cite

Neoadjuvant treatment (NAT) of breast cancer (BCa) is an option for patients with the locally advanced disease. It has been compared with standard adjuvant therapy with the aim of improving prognosis and surgical outcome. Moreover, the response of the tumor to the therapy provides useful information for patient management. The pathological examination of the tissue sections after surgery is the gold-standard to estimate the residual tumor and the assessment of cellularity is an important component of tumor burden assessment. In the current clinical practice, tumor cellularity is manually estimated by pathologists on hematoxylin and eosin (H&E) stained slides, the quality, and reliability of which might be impaired by inter-observer variability which potentially affects prognostic power assessment in NAT trials. This procedure is also qualitative and time-consuming. In this paper, we describe a method of automatically assessing cellularity. A pipeline to automatically segment nuclei figures and estimate residual cancer cellularity from within patches and whole slide images (WSIs) of BCa was developed. We have compared the performance of our proposed pipeline in estimating residual cancer cellularity with that of two expert pathologists. We found an intra-class agreement coefficient (ICC) of 0.89 (95% CI of [0.70, 0.95]) between pathologists, 0.74 (95% CI of [0.70, 0.77]) between pathologist #1 and proposed method, and 0.75 (95% CI of [0.71, 0.79]) between pathologist #2 and proposed method. We have also successfully applied our proposed technique on a WSI to locate areas with high concentration of residual cancer. The main advantage of our approach is that it is fully automatic and can be used to find areas with high cellularity in WSIs. This provides a first step in developing an automatic technique for post-NAT tumor response assessment from pathology slides.

show abstract

Section: Discussionmentioning

confidence: 99%

Automatic cellularity assessment from post‐treated breast surgical specimens

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…Web server [42,43] Tracking pathologists' behavior Eye tracking [44], mouse tracking [45] and viewport tracking [46] Active learning Uncertainly sampling [43], Query-by-Committee [47], variance reduction [48] and hypothesis space reduction [49] Multiple instance learning Boosting-based [50,51], deep weak supervision [52] and structured support vector machines (SVM) [53] Semi-supervised learning Manifold learning [30] and SVM [54] Transfer learning Feature extraction [55], fine-tuning [16,56,57]…”

Section: Gui Toolsmentioning

confidence: 99%

Machine Learning Methods for Histopathological Image Analysis

Komura

Ishikawa

2018

Computational and Structural Biotechnology Journal

644

462

View full text Add to dashboard Cite

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.

show abstract

“…13 Generating labelled datasets for supervised machine learning is laborious and time consuming and requires direct ground-truth labelling by pathologists. 14,15 Labelling pathology data (e.g. a whole slide image) requires an expert identifying a diagnostic focus or providing a diagnostic category that will be used as the diagnosis to train the algorithm (i.e.…”

Section: Ta B L E 1 Summary Of the Role Of Pathologists In Developingmentioning

confidence: 99%

The cytopathologist's role in developing and evaluating artificial intelligence in cytopathology practice

McAlpine

Michelow

2020

Cytopathology

View full text Add to dashboard Cite

Artificial intelligence (AI) technologies have the potential to transform cytopathology practice, and it is important for cytopathologists to embrace this and place themselves at the forefront of implementing these technologies in cytopathology. This review illustrates an archetypal AI workflow from project conception to implementation in a diagnostic setting and illustrates the cytopathologist's role and level of involvement at each stage of the process. Cytopathologists need to develop and maintain a basic understanding of AI, drive decisions regarding the development and implementation of AI in cytopathology, participate in the generation of datasets used to train and evaluate AI algorithms, understand how the performance of these algorithms is assessed, participate in the validation of these algorithms (either at a regulatory level or in the laboratory setting), and ensure continuous quality assurance of algorithms deployed in a diagnostic setting. In addition, cytopathologists should ensure that these algorithms are developed, trained, tested and deployed in an ethical manner. Cytopathologists need to become informed consumers of these AI algorithms by understanding their workings and limitations, how their performance is assessed and how to validate and verify their output in clinical practice.

show abstract

Clustering Analysis for Semi-supervised Learning Improves Classification Performance of Digital Pathology

Cited by 12 publications

References 10 publications

Automatic cellularity assessment from post‐treated breast surgical specimens

Automatic cellularity assessment from post‐treated breast surgical specimens

Machine Learning Methods for Histopathological Image Analysis

The cytopathologist's role in developing and evaluating artificial intelligence in cytopathology practice

Contact Info

Product

Resources

About