Breast Cancer Images Analysis Based On Deep Detection Networks And Deep Segmentation Networks

Breast cancer is the most frequently diagnosed cancer among women,and it places huge threat to women’s health.Benefited from its convenience and rapidity,mammography has become the most commonly used modality for breast cancer screening.Histopathology serves as the gold standard for the diagnosis and grading of breast cancer,because of its high accuracy.Nowadays,the diagnosis of breast cancer images is still performed by doctors manually,highly subject to the experience,working status and judgment of the doctor.Hence chances are that the efficiency and accuracy of breast cancer diagnosis fail to meet the standard.With the remarkable improvements in artificial intelligence and medical image analysis,the computer-aided diagnosis system for breast cancer has been a research focus.Mitotic count is a significant indicator in breast histopathology,while mass is a common abnormality of breast cancer in mammography.At present,the computer-aided diagnosis for the two problems still has many deficiencies.Algorithms with higher accuracy and speed are required in clinical practice.Therefore,aiming at mitosis detection in histopathology and mass based diagnosis in mammography,this dissertation utilizes deep learning based image analysis methods to study the following four aspects.(1)A descriptor based on deep neural networks is proposed in this dissertation.The Neural Features are derived from the middle layers of the fully convolutional networks,containing enough global semantical information and local detailed information.Moreover,what adds to its suitability for object detection is that Neural Features retain the structure of images,hence it is very efficient to extract region features from the whole feature map.This dissertation combines the proposed Neural Features with the traditional classifier AdaBoost,and constructs multi-scale image feature pyramids by power law for detecting multi-scale objects accurately and efficiently.By fine tuning the networks in a dataset,Neural Features can automatically learn the distribution of the specific dataset.Compared with handcrafted features,the proposed Neural Features can be applied to different datasets and tasks flexibly,with no need for domain knowledge.This method has been verified in both natural images and medical images.In Caltech pedestrian dataset,the proposed method achieves the state-of-the-art performance.While in 2012 MITOSIS dataset,it also achieves high detection performance,which demonstrates that the Neural Features can describe mitosis very well.(2)A mitosis detection network is proposed in this dissertation,which jointly optimizes the region proposal network and the proposal classification network.The method constructs an end-to-end mitosis detection network and optimizes the network globally.Convolutional features are shared between the two subnetworks so that better representations can be learned.In each subnetwork,region classification task and region regression task are learned simultaneously based on multi-task learning.The two tasks can interact each other and optimize the network jointly.To address the problem of variation in mitoses’ scales and shapes,this dissertation designs appropriate anchor boxes and image scales.Deep networks can be trained in small-scale dataset by means of transfer learning and suitable data augmentation.This dissertation proposes a mitosis detection system which combines a deep detection network,a deep segmentation network and a deep verification network.Based on transfer learning,this system utilizes a segmentation model trained on supervised data to guide the detection model trained on weakly-supervised data,in order to transfer the knowledge and capability from the segmentation model to the detection model.The verification network is trained on the result of the detection network,such that the verification network can provide the hard example mining mechanism for the entire system and improves the discriminant ability for hard negatives.Experimental results on 2012 MITOSIS dataset show that the proposed method’s F1 score outperforms the previous best method by 1% and the previous best deep learning based method by 4.4%.On the weakly-supervised 2014 MITOSIS dataset,the detection performance gap between this method and the previous best method is only 0.5%.In addition,the detection speed of the proposed method is 0.4s-0.7s per HPF,which is quite fast.(3)A concentric loss based deep segmentation network and a mitosis detection method based on segmentation maps are proposed in this dissertation,aiming at improving the performance on weakly-supervised data.The concentric loss is designed according to the characteristics of mitotic cells and annotations: a small circle contains the positive pixels,while a circular region represents an unknown region in which training loss are not calculated.By mining as many as possible positive pixels samples in weakly-supervised data,the concentric loss can train an accurate semantic segmentation model.The predication map of the segmentation model produces candidate connected region by a Gaussian filtering and an adaptive binary segmentation.On the connected regions,the mean segmentation scores and areas are defined as features,then the candidates can be screened accurately and quickly by the two features.The proposed method achieves the highest performance in multiple weakly-supervised datasets.Experiments on 2014 MITOSIS/AMIDA13 /TUPAC16 show that the F1 scores of the proposed method surpass those of the previous best methods by 8%,6% and 1.7% respectively.On the supervised 2012 MITOSIS dataset,the proposed method also obtains the state-of-the-art performance.The concentric loss has very good generalization,and its performance in mass segmentation is on par with that of models trained with pixel-level mask label.(4)A mass segmentation method in full mammography is proposed in this dissertation,and based on the segmentation result,mass detection algorithm and full-mammography malignant prognosis algorithm are also proposed.Nowaday mass detection is generally conducted in manually selected mass regions,which requires mass localization firstly and hence are of low practical value.This dissertation directly segments malignant masses in full mammography based on deep segmentation network.By making better use of context information within the network,more precise segmentation results are achieved.Mass detection is realized through calculating the malignant degree of the segmented connected region.Different full-mammography representations of benign and malignant tumors are designed for full-mammography diagnosis,including the max pooling representation based on multi-instance learning(MIL)and the average pooling representation based on the feature fusion.Multi-view images of the same breast are fused by max pooling or average pooling,which significantly improves the results of the whole-image diagnosis.Moreover,by introducing the class of benign mass,the discriminative power of the model between benign and malignant mass is improved,and the false positives produced by benign mass is reduced.The proposed method completes mass segmentation,mass detection and full-mammography diagnosis on the CBIS-DDSM benchmark,and achieves excellent performance.Therefore,the proposed method constitutes a complete mass based diagnosis system for breast cancer.