Cervical cytopathology image refocusing via multi-scale attention features and domain normalization.

Cervical cytopathology image refocusing is important for addressing the problem of defocus blur in whole slide images. However, most of current deblurring methods are developed for global motion blur instead of local defocus blur and need a lot of supervised re-training for unseen domains. In this paper, we propose a refocusing method for cervical cytopathology images via multi-scale attention features and domain normalization. Our method consists of a domain normalization net (DNN) and a refocusing net (RFN). In DNN, we adopt registration-free cycle scheme for normalizing the unseen unsupervised domains into the seen supervised domain and introduce gray mask loss and hue-encoding mask loss to ensure the consistency of cell structure and basic hue. In RFN, combining the locality and sparseness characteristics of defocus blur, we design a multi-scale refocusing network to enhance the reconstruction of cell nucleus and cytoplasm, and introduce defocus intensity estimation mask to strengthen the reconstruction of local blur. We integrate hybrid learning strategy on the supervised and unsupervised domains to make RFN achieving well refocusing on the unsupervised domain. We build a cervical cytopathology image refocusing dataset and conduct extensive experiments to demonstrate the superiority of our method compared with current deblurring state-of-the-art models. Furthermore, we prove that the refocused images help improve the performance of subsequent high-level analysis tasks. We release the refocusing dataset and source codes to promote the development of this field.

Robust whole slide image analysis for cervical cancer screening using deep learning.

Computer-assisted diagnosis is key for scaling up cervical cancer screening. However, current recognition algorithms perform poorly on whole slide image (WSI) analysis, fail to generalize for diverse staining and imaging, and show sub-optimal clinical-level verification. Here, we develop a progressive lesion cell recognition method combining low- and high-resolution WSIs to recommend lesion cells and a recurrent neural network-based WSI classification model to evaluate the lesion degree of WSIs. We train and validate our WSI analysis system on 3,545 patient-wise WSIs with 79,911 annotations from multiple hospitals and several imaging instruments. On multi-center independent test sets of 1,170 patient-wise WSIs, we achieve 93.5% Specificity and 95.1% Sensitivity for classifying slides, comparing favourably to the average performance of three independent cytopathologists, and obtain 88.5% true positive rate for highlighting the top 10 lesion cells on 447 positive slides. After deployment, our system recognizes a one giga-pixel WSI in about 1.5 min.

An unsupervised style normalization method for cytopathology images.

Diverse styles of cytopathology images have a negative effect on the generalization ability of automated image analysis algorithms. This article proposes an unsupervised method to normalize cytopathology image styles. We design a two-stage style normalization framework with a style removal module to convert the colorful cytopathology image into a gray-scale image with a color-encoding mask and a domain adversarial style reconstruction module to map them back to a colorful image with user-selected style. Our method enforces both hue and structure consistency before and after normalization by using the color-encoding mask and per-pixel regression. Intra-domain and inter-domain adversarial learning are applied to ensure the style of normalized images consistent with the user-selected for input images of different domains. Our method shows superior results against current unsupervised color normalization methods on six cervical cell datasets from different hospitals and scanners. We further demonstrate that our normalization method greatly improves the recognition accuracy of lesion cells on unseen cytopathology images, which is meaningful for model generalization.