Back to Search
Start Over
FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology
- Source :
- Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597214, MICCAI (5)
- Publication Year :
- 2020
- Publisher :
- Springer International Publishing, 2020.
-
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.
- Subjects :
- 0303 health sciences
Computer science
business.industry
Digital pathology
02 engineering and technology
Machine learning
computer.software_genre
Stain
03 medical and health sciences
Microscopy
0202 electrical engineering, electronic engineering, information engineering
Whole slide image
020201 artificial intelligence & image processing
Artificial intelligence
Focus (optics)
business
computer
030304 developmental biology
Subjects
Details
- ISBN :
- 978-3-030-59721-4
- ISBNs :
- 9783030597214
- Database :
- OpenAIRE
- Journal :
- Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597214, MICCAI (5)
- Accession number :
- edsair.doi...........10f197dce6c3926616aac0589808a2d5