Meng Yan

Yixiong Liang, Yuan Mao, Zhihong Tang et al.

In this paper we propose a very efficient method to fuse the unregistered multi-focus microscopical images based on the speed-up robust features (SURF). Our method follows the pipeline of first registration and then fusion. However, instead of treating the registration and fusion as two completely independent stage, we propose to reuse the determinant of the approximate Hessian generated in SURF detection stage as the corresponding salient response for the final image fusion, thus it enables nearly cost-free saliency map generation. In addition, due to the adoption of SURF scale space representation, our method can generate scale-invariant saliency map which is desired for scale-invariant image fusion. We present an extensive evaluation on the dataset consisting of several groups of unregistered multi-focus 4K ultra HD microscopic images with size of 4112 x 3008. Compared with the state-of-the-art multi-focus image fusion methods, our method is much faster and achieve better results in the visual performance. Our method provides a flexible and efficient way to integrate complementary and redundant information from multiple multi-focus ultra HD unregistered images into a fused image that contains better description than any of the individual input images. Code is available at https://github.com/yiqingmy/JointRF.

Yixiong Liang, Zhihong Tang, Meng Yan et al.

Automated detection of cervical cancer cells or cell clumps has the potential to significantly reduce error rate and increase productivity in cervical cancer screening. However, most traditional methods rely on the success of accurate cell segmentation and discriminative hand-crafted features extraction. Recently there are emerging deep learning-based methods which train convolutional neural networks (CNN) to classify image patches, but they are computationally expensive. In this paper we propose an efficient CNN-based object detection methods for cervical cancer cells/clumps detection. Specifically, we utilize the state-of-the-art two-stage object detection method, the Faster-RCNN with Feature Pyramid Network (FPN) as the baseline and propose a novel comparison detector to deal with the limited data problem. The key idea is that classify the proposals by comparing with the reference samples of each category in object detection. In addition, we propose to learn the reference samples of the background from data instead of manually choosing them by some heuristic rules. Experimental results show that the proposed Comparison Detector yields significant improvement on the small dataset, achieving a mean Average Precision (mAP) of 26.3% and an Average Recall (AR) of 35.7%, both improving about 20 points compared to the baseline. Moreover, Comparison Detector improved AR by 4.6 points and achieved marginally better performance in terms of mAP compared with baseline model when training on the medium dataset. Our method is promising for the development of automation-assisted cervical cancer screening systems. Code is available at https://github.com/kuku-sichuan/ComparisonDetector.