MACD R-CNN: An Abnormal Cell Nucleus Detection Method

Abstract

The detection of abnormal cell nuclei is a key technique of the cytopathic automatic screening system, which directly determines the performance of the system. Although the Mask R-CNN which combines target detection and semantic segmentation has achieved good performance in general target detection tasks, the performance in abnormal cell detection is still unsatisfactory. To solve this problem, we design a new deep neural network for abnormal cell detection based on the Mask R-CNN, named mask abnormal cell detection R-CNN (MACD R-CNN). First, in the classification branch of Mask R-CNN, it generates the same size of feature maps from different size of RoIs as the input. The nuclei in this part of the feature maps will be deformed to varying degrees. We design a fixed proposal module to generate fixed-sized feature maps of nuclei, which allows the new information of nucleus is used for classification. Then we use the attention mechanism to merge the original RoI and Fixed RoI features. Finally, we increase the depth of the convolution layer to further improve the accuracy of cell classification. Experiments show that the MACD R-CNN can effectively improve the performance of abnormal cell detection.

Learning Pseudo Scale Instance Maps for Cell Localization

“…Alam et al [12] use a modified YOLO network to automate the detection and counting of red blood cells, white blood cells, and platelets. In addition to the YOLO series of networks, Ma et al [13] propose an abnormal cell detection network based on Mask R-CNN, which integrates different features using an attention mechanism to improve detection performance. Kutlu et al [10] introduce a deep learning and transfer learning-based approach for automatic leukocyte detection from blood smear images.…”

AttentionMask R‐CNNwith edge refinement algorithm for identifying circulating genetically abnormal cells

“…Mask R‐CNN is a well‐known instance segmentation and target detection model [15]. Improved Mask R‐CNN‐based approaches have been widely used in cell segmentation [16], cancer detection [17] and other fields [18]. Ma et al used fixed‐size region proposals and an attention mechanism with Mask R‐CNN (MACD R‐CNN) for the detection of abnormal nuclei [16].…”

“…Improved Mask R‐CNN‐based approaches have been widely used in cell segmentation [16], cancer detection [17] and other fields [18]. Ma et al used fixed‐size region proposals and an attention mechanism with Mask R‐CNN (MACD R‐CNN) for the detection of abnormal nuclei [16]. Xi et al proposed a multipath fusion Mask R‐CNN with double attention (DAMF Mask R‐CNN) to implement the simultaneous segmentation of tooth surface and gear pitting [17].…”

“…Mask R-CNN is a well-known instance segmentation and target detection model [15]. Improved Mask R-CNN-based approaches have been widely used in cell segmentation [16], cancer detection [17] and other fields [18]. Ma 1A).…”