Challenges: ⢠Background: ⢠Related works: Metastatic ... challenge dataset of Camelyon-16, which contains 280. WSIs for training and 130 WSIs for testing.
ScanNet: A Faster and Dense Scanning Framework for Metastatic Breast Cancer Detection from Whole-Slide Image
WACV2018 IEEE Winter Conf. on Applications of Computer Vision
Huangjing Lin1, Hao Chen1,2, Qi Dou1, Liansheng Wang3, Jing Qin4, and Pheng-Ann Heng1 Dept. of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China 2 Imsight Medical Technology Inc., China 3 Dept. of Computer Science and Technology, Xiamen University, Xiamen, China 4 Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China 1
Introduction • Background:
• Challenges: (1) giga-pixel level image; (2) existence of hard mimics; (3) a large variations in size, staining appearance and biological structure.
(1) We preprocess whole-slide image by OTSU on level-5 to remove the non-informative regions.
Localization: Free Response Operation Characteristic curve (FROC Score). Classification: Receiver Operating Characteristic curve (AUC Score)
level-1
Offset ROIs
level-0
Zoom in
Probability Tiles by Offsets
Denser Probability Tile
Detection on offset ROIs
Reconstruction
Fig. 3. Dense Reconstruction
Fig. 1. Preprocessing by OTSU with multi-level mapping strategy
Experimental Results
ROI1 ROI2
• Contributions:
• Divide and Conquer:
The evaluation metrics:
Table. 2. Quantitative comparison with other methods
=
Multi-level Mapping
Different patch-wised convolutional networks : GoogleNet, ResNet-34, AlexNet, VGG, etc.
Method
Aiming to overcome the shortcoming of ScanNet in its unmodifiable scanning stride, we propose a reconstruction mechanism, which can generate high-quality dense predictions with significant improvement in performance.
level-5
• Related works:
(1) a novel framework, referred as ScanNet, leveraging fully convolutional architecture for efficient inference; (2) a novel dense reconstructing mechanism for ensuring accurate detection on both micro- and macrometastases; (3) state-of-the-art performance with a faster speed, even surpassing experienced pathologists.
• Dense reconstruction for accuracy
OTSU
……
Metastatic detection is crucial for breast cancer diagnosis and greatly affects the resection decisions from the surgeons. Typical automated metastatic detection systems are based on patch-wised scanning frameworks, which are suboptimal in whole-slide image analysis for their unbearable computational cost.
(2) Given the giga-pixel size of the image, we divide the preprocessed tissue areas into ROIs, which can be conquered by ScanNet respectively. (3) Finally, the divided results are stitched together as a integrated result.
Fig. 5. FROC curve and ROC curve
• Dataset Pre-processing
Post-processing
Efficient Inference and Heat map Stitching
Fig. 2. The framework of our method: divide and conquer wholeslide image;
We validated our method on the large-scale public challenge dataset of Camelyon-16, which contains 280 WSIs for training and 130 WSIs for testing.
• Time performance
Evaluated on an approximately 2,800×2,800 sized ROI with stride 32 and 16.
• Qualitative Evaluation
1.0
• Fast detection via ScanNet We establish a fast scanning framework (i.e., ScanNet)
Table. 3. Efficiency comparison with others (unit: minute)
based on fully convolutional architecture and VGG-16.
Conclusion
It is trained on extensive augmented data patch-wisedly,
0.5
but can predict on arbitrarily
Fig. 4. Qualitative results
sized ROIs efficiently with the
• Comparison with other methods
merit of fully convolutional architecture.
Table. 1. The architecture of ScanNet
We present a novel framework with dense reconstruction strategy to efficiently and accurately detect breast cancer metastases from whole-slide images. Our method is extensible to other applications and could inspire more intelligent learning strategy.
