CNN-GRNN for Image Sharpness Assessment

Yu, Shaode; Jiang, Fan; Li, Leida; Xie, Yaoqin

doi:10.1007/978-3-319-54407-6_4

Cited by 9 publications

(5 citation statements)

References 35 publications

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“…A variety of metrics are used across these works to evaluate accuracy and transferability. These include Area under the Receiver Operating Characteristics (ROC) and Precision Recall (PR) Curves 10,18 , F-Scores 16, 17 , Pearson's Linear Correlation Coefficient (PLCC) and Spearman's Rank Correlation Coefficient (SRCC) 2,10,11,14,15,[19][20][21][22] , Root Mean Square Error (RMSE) 12,20 , post-FQA qualitative assessment 13,20 , and Rand Index 17 . The most popular metrics of this list, including PLCC, SRCC, ROC, and PR, are therefore evaluated in this paper to clearly compare the network performance.…”

Section: Figurementioning

confidence: 99%

“…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%

“…1. Lightweight CNNs: these refer to CNNs that are optimized towards efficiency by containing only one convolutional layer [10][11][12][13][14][15]22 . Some of these networks are able to perform FQA even more quickly than knowledge-based methods 14,15 .…”

Section: Introductionmentioning

confidence: 99%

“…They also directly address the scalability problem, as their low computational complexity allows gigabytes of WSI data to be assessed for focus quality in a matter of hours. Shallow CNNs can also be well trained using a limited number of samples 14,22 , which reduces the effort needed for training. These networks generally have worse performance than deeper CNNs, but can still provide reasonable FQA performance because blur is assumed to be encoded in low-level features in a well-controlled environment 10,22 .…”

Section: Introductionmentioning

confidence: 99%

“…Shallow CNNs can also be well trained using a limited number of samples 14,22 , which reduces the effort needed for training. These networks generally have worse performance than deeper CNNs, but can still provide reasonable FQA performance because blur is assumed to be encoded in low-level features in a well-controlled environment 10,22 .…”

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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