  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.
