FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology

Wang, Zhongling; Hosseini, Mahdi S.; Miles, Adyn; Plataniotis, Konstantinos N.; Wang, Zhou

doi:10.1007/978-3-030-59722-1_39

Cited by 18 publications

(38 citation statements)

References 19 publications

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“…As a result of these challenges, the availability of quality assessment tools is currently limited, with only a small number developed specifically for histopathology slides [17][18][19][20][21][22][23] . To date, the available tools employ traditional hand-crafted features rather than learned ones 20,23 , or tend to be limited to identification of out-of-focus regions only [17][18][19][20] or identification of one artefact per image 21,22 . However, assessment for a combination of artefacts is more meaningful as in real life image artefacts are rarely limited to one feature such as poor staining or tissue folding, particularly with older glass slides.…”

Section: /18mentioning

confidence: 99%

PathProfiler: Automated Quality Assessment of Retrospective Histopathology Whole-Slide Image Cohorts by Artificial Intelligence – A Case Study for Prostate Cancer Research

Haghighat

Browning

Sirinukunwattana

et al. 2021

Preprint

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Research using whole slide images (WSIs) of scanned histopathology slides for the development of artificial intelligence (AI) algorithms has increased exponentially over recent years. Glass slides from large retrospective cohorts with patient follow-up data are digitised for the development and validation of AI tools. Such resources, therefore, become very important, with the need to ensure that their quality is of the standard necessary for downstream AI development. However, manual quality control of such large cohorts of WSIs by visual assessment is unfeasible, and whilst quality control AI algorithms exist, these focus on bespoke aspects of image quality, e.g. focus, or use traditional machine-learning methods such as hand-crafted features, which are unable to classify the range of potential image artefacts that should be considered. In this study, we have trained and validated a multi-task deep neural network to automate the process of quality control of a large retrospective cohort of prostate cases from which glass slides have been scanned several years after production, to determine both the usability of the images for research and the common image artefacts present. Using a two-layer approach, quality overlays of WSIs were generated from a quality assessment undertaken at patch-level at 5X magnification. From these quality overlays the slide-level quality scores were predicted and then compared to those generated by three specialist urological pathologists, with a Pearson correlation of 0.89 for overall usability (at a diagnostic level), and 0.87 and 0.82 for focus and H&E staining quality scores respectively. We subsequently applied our quality assessment pipeline to the TCGA prostate cancer cohort and to a colorectal cancer cohort, for comparison. Our model, designated as PathProfiler, indicates comparable predicted usability of images from the cohorts assessed (86-90%), and perhaps more significantly is able to predicts WSIs that could benefit from re-scanning or re-staining for quality improvement. We have shown in this study that AI can be used to automate the process of quality control of large retrospective cohorts to maximise research outputs and conclusions.

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Section: /18mentioning

confidence: 99%

PathProfiler: Automated Quality Assessment of Retrospective Histopathology Whole-Slide Image Cohorts by Artificial Intelligence – A Case Study for Prostate Cancer Research

Haghighat

Browning

Sirinukunwattana

et al. 2021

Preprint

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“…Recently, deep learning models based on convolutional neural networks (CNNs) have emerged as viable FQA methods 2,[10][11][12][13][14][15][16][17][18][19][20][21][22][23] . Open source platforms such as HistoQC 13 , CellProfiler 3.0 17 and ImageJ 24,25 also leverage deep learning models for FQA.…”

Section: Introductionmentioning

confidence: 99%

“…However, two major barriers have been slowing down the adoption of deep learning methods in clinical workflows. The first is their undertested transferablity to diverse imaging conditions, and the second is their potentially high computational cost and scalability to extremely high scanning throughput in practical clinical workflows 3,10 .…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, a principal advantage of digital pathology technology is that scanners can process images much faster than human pathologists, with some able to scan hundreds of images at a time 6,26,40 . In clinical settings, it is ideal for scans to be completed during night-time hours, so that they are ready for diagnosis the following day 10 . The high throughput of digital scans therefore requires QC pipelines that can handle this high throughput.…”

Section: Introductionmentioning

confidence: 99%

“…The high throughput of digital scans therefore requires QC pipelines that can handle this high throughput. Computational complexity of the FQA method should not be a restricting factor, and is therefore equally important in the evaluation of the method as its performance 10,40,41 .…”

Section: Introductionmentioning

confidence: 99%

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Transferability of Deep Learning Models for Focus Quality Assessment in Digital Pathology

Miles

Hosseini

Tang

et al. 2021

Preprint

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Out-of-focus sections of whole slide images are a significant source of false positives and other systematic errors in clinical diagnoses. As a result, focus quality assessment (FQA) methods must be able to quickly and accurately differentiate between focus levels in a scan. Recently, deep learning methods using convolutional neural networks (CNNs) have been adopted for FQA. However, the biggest obstacles impeding their wide usage in clinical workflows are their generalizability across different test conditions and their potentially high computational cost. In this study, we focus on the transferability and scalability of CNN-based FQA approaches. We carry out an investigation on ten architecturally diverse networks using five datasets with stain and tissue diversity. We evaluate the computational complexity of each network and scale this to realistic applications involving hundreds of whole slide images. We assess how well each full model transfers to a separate, unseen dataset without fine-tuning. We show that shallower networks transfer well when used on small input patch sizes, while deeper networks work more effectively on larger inputs. Furthermore, we introduce neural architecture search (NAS) to the field and learn an automatically designed low-complexity CNN architecture using differentiable architecture search which achieved competitive performance relative to established CNNs.

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