FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology

• URL: http://arxiv.org/abs/2007.06565v2
• Date: Thu, 1 Oct 2020 17:21:55 GMT
• Title: FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology
• Authors: Zhongling Wang, Mahdi S. Hosseini, Adyn Miles, Konstantinos N. Plataniotis, Zhou Wang
• Abstract summary: We propose a CNN-based model that maintains fast computations similar to the knowledge-driven methods without excessive hardware requirements. We create a training dataset using FocusPath which encompasses diverse tissue slides across nine different stain colors. In our attempt to reduce the CNN complexity, we find with surprise that even trimming down the CNN to the minimal level, it still achieves a highly competitive performance.
• Score: 42.531674974834544
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Out-of-focus microscopy lens in digital pathology is a critical bottleneck in high-throughput Whole Slide Image (WSI) scanning platforms, for which pixel-level automated Focus Quality Assessment (FQA) methods are highly desirable to help significantly accelerate the clinical workflows. Existing FQA methods include both knowledge-driven and data-driven approaches. While data-driven approaches such as Convolutional Neural Network (CNN) based methods have shown great promises, they are difficult to use in practice due to their high computational complexity and lack of transferability. Here, we propose a highly efficient CNN-based model that maintains fast computations similar to the knowledge-driven methods without excessive hardware requirements such as GPUs. We create a training dataset using FocusPath which encompasses diverse tissue slides across nine different stain colors, where the stain diversity greatly helps the model to learn diverse color spectrum and tissue structures. In our attempt to reduce the CNN complexity, we find with surprise that even trimming down the CNN to the minimal level, it still achieves a highly competitive performance. We introduce a novel comprehensive evaluation dataset, the largest of its kind, annotated and compiled from TCGA repository for model assessment and comparison, for which the proposed method exhibits superior precision-speed trade-off when compared with existing knowledge-driven and data-driven FQA approaches.

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