A Novel Two-Stage Deep Method for MitosisDetection in Breast Cancer Histology Images

Minglin Ma:, Yinghuan Shi:, Wenbin Li:, Yang Gao:*, and Jun Xu;

:State Key Laboratory for Novel Software Technology, Nanjing University, China;Jiangsu Key Laboratory of Big Data Analysis Technique,

Nanjing University of Information Science and Technology, China

Abstract—The accurate detection and counting of mitosis inbreast cancer histology images is very important for computer-aided diagnosis, which is manually completed by the pathologistaccording to her or his clinic experience. However, this procedureis extremely time consuming and tedious. Moreover, it alwaysresults in low agreement among different pathologists. Althoughseveral computer-aided detection methods have been developedrecently, they suffer from high FN (false negative) and FP (falsepositive) with simply treating the detection task as a binaryclassification problem. In this paper, we present a novel two-stage detection method with multi-scale and similarity learningconvnets (MSSN). Firstly, large amount of possible candidateswill be generated in the first stage in order to reduce FN (i.e.,prevent treating mitosis as non-mitosis), by using the differentsquare and non-square filters, to capture the spatial relationfrom different scales. Secondly, a similarity prediction modelis subsequently performed on the obtained candidates for thefinal detection to reduce FP, which is realized by imposing alarge margin constraint. On both 2014 and 2012 ICPR MITOSISdatasets, our MSSN achieved a promising result with a highestRecall (outperforming other methods by a large margin) and acomparable F-score.

I. INTRODUCTION

Currently, breast cancer is becoming one of the mainthreats to woman health, which acts as one of the mostleading causes of cancer-related death over the world [1].For breast cancer treatment, the cells and tissue structures(e.g., mitotic counts) of cancerous breast are usually examinedby the experienced pathologist to determine the grade ofcancer severity. The mitosis detection and counting could helpdetermine the uncontrollability of the cells, which acts asthe hallmark of breast cancer: the more dividing cells, theworse the cancer will be. However, in actual clinical workflow,the traditional human-based examination normally requiresthe pathologist to manually count the dividing cells one-by-one in a microscope. The major limitations of this human-based examination include that: (1) laborious time cost due tolarge amount of images, (2) subjective results due to differentexperience, and (3) incorrect results due to some negligence,e.g., fatigue.

It is therefore an important task to develop computer-aidedmitosis detection methods that can help clinical workflow toalleviate the laborious workload of the pathologist. However,automatic mitosis detection in histology images is a challeng-ing task including the following reasons. First, in cell biology,

Mitotic nuclei

Non-mitotic

SVM＋

Mitotic nuclei

Non-mitotic

Mimics

Mimic Similarity＋

False Positive

Reduce FP

×

√

√

×

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True mitosis

Single scale feature

×

×

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×

√

Mitoses in the training set

False negativesNon-mitoses in the training set

The differences between our methods and traditional methods.

False positives

False negatives

Multi-scale feature

Candidate

(Two classes) (Classification)

(Three classes) (Metric Learning)

Fig. 1. Examples of the mitosis with various sizes and shapes (greenrectangle), and the FP candidates (yellow rectangle) which carry similarappearance. We also show a FN candidate in different scales (72 ˆ 72, 96ˆ 96, 60 ˆ 120, 120 ˆ 60, 120 ˆ 120) (blue rectangle) and a non-mitoticnucleus that carry similar appearance in different scales (purple rectangle).The differences between our method MSNN and traditional methods are alsoillustrated in the figure.

mitosis is a part of cell cycle when replicated chromosomesare separated into two new nuclei. The process of mitosis isdivided into several stages [7]. The shape varies greatly atdifferent stages, which leads to plenty of sizes and shapes asshown in the green rectangle of Figure 1. Second, during thecell cycle process, many other cells (in the yellow rectangle ofFigure 1) carry similar appearances with the mitoses, makingtraditional methods produce many false positives. Last, mitosisdetection is a class imbalance detection task, in which thenumber of dividing cells is much less than that of other cells.For example, a 2000 ˆ 1500 histology image only containsone or several (ď10) mitotic nuclei, while the other regionsare all labeled as non-mitotic nuclei.

Many researchers have devoted several efforts [19] [12][23] to develop efficient and robust methods for mitosis de-tection using either handcrafted features or deep features [13].However, these previous methods normally treat this task asa single-stage binary classification problem, which usuallysuffers from relative high false negative (FN) and false positive

2018 24th International Conference on Pattern Recognition (ICPR)Beijing, China, August 20-24, 2018

978-1-5386-3788-3/18/$31.00 ©2018 IEEE 3892

(FP). Typically, FN indicates the percentage of classifying themitoses as non-mitoses, while FP indicates the percentage ofclassifying the non-mitoses as mitoses.

Thus, reducing FN and FP are very crucial for performanceimprovement. For the reasons of high FN, most of the previousmethods [5] usually train a classifier by employing a singlescale patch (e.g., 100 ˆ 100) which is the crime culpritof FN. This single scale patch drives the classifier firmlybelieve that mitotic nuclei are just black points. We showan FN candidate in blue rectangle in Figure 1 and a non-mitotic nucleus in purple rectangle in Figure 1. We caneasily obtain an interesting observation: in 72 ˆ 72 patch,the FN mitosis and the non-mitotic nucleus are too similarto distinguish, due to both of them appear as black points.However, when we compare the patches in other scales, wecan easily distinguish between them. This observation revealsthe importance of the contextual information. In a single scale,the mitoses are considered as FN. While in other scales, usingmore contextual information, FN will be reduced because theadditional contextual information helps the classifier make adistinction between them. For the reasons of high FP, whenwe observe the FP in yellow rectangle in Figure 1, wefind that the FP generated by traditional methods are reallyunderstandable. This is because the FP candidates show verysimilar appearance that the classifier cannot distinguish due tothe training patches belong to only two classes: mitotic andnon-mitotic. Meanwhile, the traditional classification networksare usually designed for natural images. The appearances ofthe natural images vary widely (e.g., some object classes inPASCAL VOC dataset [6] include: person, bird, cat, boat,etc.), However, If we use the FP as another category (mimic)to train a deep metric learning network [24] which is designedfor distinguishing similar objects to separate the mimics andmitoses, the FP will be reduced at the same time.

In this paper, we present a two-stage method to combinemulti-scale classification network and similarity predictionnetwork (see Figure 2) to detect mitosis. The multi-scale clas-sification network is used for FN reduction and the similaritynetwork is used for FP reduction. In the first stage, our modelgenerates enough candidates to enormously reduce the FNby taking full advantages of the contextual information. Inthe second stage, all obtained candidates will be input into asimilarity prediction network which was trained by three kindsof patches. The advantage of the deep metric leaning methodcan be employed to reduce the FP.

Our main contributions can be summarized as: (1) Wedeeply analyze the causes for high FN and FP in previousmitosis detection methods, and then propose a novel two-stage detection method (termed as MSSN) by incorporatingthe multi-scale and similarity prediction networks. (2) Forreducing FN, we design a multi-scale network in the first stage,to generate a large amount of possible candidates, which willseldom classify the non-mitosis as mitosis. (3) For reducingFP, we design a similarity prediction network in the secondstage, by imposing a large margin constraint to further learnthe discriminative feature representation for mitotic nuclei,

Feature 1

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×

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Original histology image

Scan the image with

a multi-scale window

Features learned from the dataset.

Matching one of the features.

Enough candidates with some FP and few FN

( FNR-model )

A true mitosis

or

A mimic

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Final results with few FN and FP All candidates

( Reduce FP )

CN

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)C

NN

(Y)

|| C

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N(Y

) ||

Mitosis

Mimics

Non-mitosis

( FPR-model )Similarity prediction network

Fig. 2. The framework of our MSSN. CNN(X) and CNN(Y) indicate featureslearned from a convolutional neural network. The goal of the FNR-model (firststage) is to generate candidates and FN reduction. The goal of FPR-model(second stage) is to distinguish mimic and FP reduction.

non-mitotic nuclei, and mimics. (4) We validated our MSSNon the ICPR 2014 and 2012 challenge datasets. Comparedwith the state-of-the-art methods, our method could obtain thebest Recall and the comparable F-score at the same time.

II. RELATED WORKS

In recent years, a series of methods have been pro-posed [3] [5] for the task of automated mitosis detection.Several methods adopted the handcrafted features to capturecertain morphological, statistical or textural attributes of mi-toses. Sommer et al. proposed a hierarchical learning workflowwith a pixel-wise classifier for automated mitosis detection inbreast cancer [19]. Khan et al. proposed a statistical approachwhich modeled the pixel intensities in mitotic and non-mitoticregions by a Gamma Gaussian mixture model [12]. Tekinvestigated an application of a set of generic features andan ensemble of cascade adaboosts to the automated mitosisdetection [20]. Irshad et al. aimed to improve the detectionaccuracy by selecting the color channels that better capturethe statistical and morphological features [11]. These previousmethods using handcrafted features, which are inspired by thedomain-specific design, cannot handle the high variable sizesand shapes of mitoses very well. Recently, with the remarkablesuccesses of deep convolutional neural networks (CNNs) onobject detection in natural images [8] [9] [17] [16]. This alsoinspired the researchers to employ CNNs in automated mitosisdetection. Ciresan et al. trained networks to classify each pixelin the images [5], and their approach won the ICPR 2012mitosis detection competition [18]. Chen et al. designed acascaded CNN which is composed of a FCN network [14] anda fine discrimination model [3]. Their approach outperformedother methods in 2014 ICPR MITOSIS challenge. To take fulladvantages of handcrafted features and CNN features, severalmethods learned multiple attributes that characterize mitosisvia the combination of CNN and handcrafted features. Wanget al. presented a cascaded approach to combine the CNNand handcrafted features for mitosis detection [23]. Existingmethods just aimed to complete the detection as a binaryclassification task. The FN and FP are still relative high. We

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Multi Detection Operator

72×72

96×96

Cross-bar

Dilated Convolution

Feature 1

Feature 2

Feature 3

Feature 4 √

×

×

×

A candidate

Match one

of the features

Fig. 3. An overview of the FNR-model. Four features including two traditionalsquare features and two novel features (cross-bar and dilated convolution) areextracted to generated candidates.

analyze the causes of the FN and FP and propose a noveltwo-stage method by dividing the detection process into twostages for reducing the FN and FP.

III. OUR MSSN

The proposed MSSN will be illustrated in this section. Thepipeline includes two main models, the FNR-model and FPR-model, integrated into a cascade scheme (see Figure 2).

A. False Negative Reduction Model (FNR-model)

The overview of the FNR-model is shown in Figure 3. Givenan initial image I , four sub-models will analyze the imageI separately. Each sub-model generates its own result whichfocuses on different contextual information. Afterwards, wecombine the results these sub-models generated together.

Multi-Scale Detection Operator: Traditional methodswhich train their models using a single scale patch drive theclassifiers firmly believe that mitotic nuclei are just blackpoints, leading to many FN. Actually in clinical practice,pathologists will not just observe a fixed scale of region. Theywill observe the surrounding regions, and then decide whetherthese regions are the target regions. The amount of surroundingcontextual information will implicitly and greatly influence thegenerated prediction probability distribution and the accuracyof detection results. In principle, if the size of training patchesis too small, only limited contextual information is exploitedand its discrimination capability should be deficient to handlelarge variations of detection targets. On the other hand, if thesize is too large, more redundant messages or even noise wouldbe involved in the training. We determine the sizes of trainingpatches carefully and propose to design a multi-scale detectionoperator in different scales, including size 72 ˆ 72, 96 ˆ 96,120 ˆ 120, 60 ˆ 120, 120 ˆ 60 (see Figure 3).

Cross-Bar Patches: Besides extracting patches in squareas most of the traditional methods [5] [23], we propose anovel way to extract patches in a cross-bar mode as shown inFigure 3. The cross-bar patches are able to encompass mostshapes of mitoses with proper amount of context. In actually,this non-square patch size aims to provide rich context forsmall sized mitotic nuclei and suitable amount of contextualinformation for middle sized mitotic nuclei. After extractingthe vertical and horizontal patches, they are input into two sub-models. The features of these two sub-models will be fused

Image classification

Similar image classification

Fig. 4. We regard nuclei (bird [21]) classification task as similar imageclassification task that we learn a similarity function to distinguish theminstead of classifying them [6].

and then the fused features will be used to train a classifier.We think this way is more reasonable, because the vertical andthe horizontal patches contain interrelated and complementarycontextual information.

Dilated Convolution: Meanwhile, we construct anothersub-model with a receptive field of 120 ˆ 120. This patchbounds enough contextual information. To a certain extent, the120ˆ 120 size takes the risk of bringing in noisy signals. Weintroduce a dilated convolution, which enjoys a broader viewof the input, to capture more contextual information [25]. Insimple terms, dilated convolution is just a convolution appliedto input with defined gaps. Dilation rate k “ 1 is normalconvolution and k “ 2 means skipping one pixel per input,respectively. The dilated convolution is a way of increasingreceptive view of the network exponentially. For our task,the 120 ˆ 120 patch size is too large, bringing in manyredundant messages or even noises. If each pixel in this patchis labeled by its corresponding class, it will introduce manymore parameters to learn. In contrast, dilated convolution cangreatly reduce parameters while increasing the receptive view.

B. False Positive Reduction Model (FPR-model)

Given candidates set R generated by the FNR-model, whichare still doped with many FP, the FPR-model is designed tofurther reduce these FP. In the second stage, each candidatewill go through a sampling operation first. We sample patcheswhich are apart from the center pixel of candidates less than 2pixels. All the sampled patches extracted from the candidateswill be input into a similarity prediction network. The finalclass of the candidate depends on the output of these patchesby this network. Candidates which are regarded as mimics bythe similarity net will be removed from R. When all candidatesin R have been input into the FPR-model, the last candidatesset will be returned as the final list of detections. The overviewof the FPR-model is shown in Figure 5.

Sampling: Given a candidate Ci from the last combinedresults of FNR-model, we sample patches Cij which are apartfrom the center pixel of Ci less than 2 pixels. All the sampledpatches will be input into a similarity prediction network.The class of this candidate Y depends on the output of thesepatches by this network. Cij1 (high probability score) meansthe sampled patch is more similar to mitotic nuclei and Cij0

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CNN CNN

X- X+

|| CNN(X-) - CNN(X+) ||

Comparator

Mitosis

Mimics

Non-mitosis

Original image

Patches extracted from the image

Data distribution

Similarity prediction net

Data distribution

Fig. 5. An overview of the FPR-model. X´ and X` are a pair of patcheswith different labels. CNN indicates a convolutional neural network.

(low probability score) means the patch is more similar to hardmimics or non-mitotic nuclei. It is defined as

R “ tC1, C2 ¨ ¨ ¨Ciu, Ci “ tCi1, Ci2 ¨ ¨ ¨Ciju

Y “

#

1, ifř

Cij1 ąř

Cij0

0, else ,

where R is candidates set, Cij is the j-th patch sampled fromthe candidate Ci, and Y means the label of the candidate Ci.

Similarity Prediction: In the past few years, deep featurescombined with a classifier (e.g., SVM) have been widely used,especially in object classification and detection. However, theappearances of the objects in natural images vary widely (e.g.,some object classes in PASCAL VOC dataset are: person, bird,cat, boat, etc.). While in our task, the hard mimics carry verysimilar appearance to the real mitoses that classification mod-els cannot easily distinguish them. As shown in Figure 4, weregard nuclei classification task as similar image classificationtask. Our goal is to learn a similarity function that measureshow similar or related two patches are by maximizing thedistance between different classes. Inspired by the Siamesenetwork [2] [4] [10]. We utilize a contrastive loss to train thenetwork to distinguish between similar and dissimilar pairs ofexamples. The process is defined as

EθpX1, X2q “ }GθpX1q ´GθpX2q} ,

Loss`

θ, pY,X1, X2qk˘

“ p1´Y qmax`

0,M´EθpX1, X2qk˘

`Y`

EθpX1, X2qk˘

,

where X1 and X2 are a pair of patches randomly selectedfrom the dataset, and GθpX1q and GθpX2q are two represen-tations learned from a deep network. EθpX1, X2q measuresthe similarity (distance) between X1 and X2. θ indicatesthe parameters of the network, M is the constant marginparameter, and k means the k-th sample. The goal is to gaina small distance between similar pairs (Y “ 1), and largedistance between dissimilar pairs (Y “ 0). For our purposeof reducing FP in mitosis detection, we design the similarityprediction network which are trained by three kinds of patches,i.e, mitotic nuclei, non-mitotic nuclei and hard mimics (seeFigure 5). The similarity prediction network can give thesimilarity probability score of two patches (high probabilityscore for same pair), as a result of gathering similar patches insame space and dividing different patches in different spaces.

Single Sub-model FNR-model FPR-model Ground Truth

Fig. 6. Typical results by our method on the testing data of ICPR 2014MITOSIS contest. The first column shows the results of a single sub-model(i.e., dilated convolution on 120ˆ 120 patch) and the second column showsthe results of combined results of the FNR-model. The third column showsthe results of the FPR-model (final detection results). The last column showsthe ground truth.

In the end, the final list of detections will be generated.From the initial input image to the final list of detections, ourproposed method is entirely end-to-end. Both FNR-model andFPR-model are implemented as convolutional neural networks.During the training, two models (i.e., FNR-model and FPR-model) were learned by stochastic gradient descent. Eachiteration of the parameter updating was computed based ona mini-batch of training samples.

IV. EXPERIMENTS

A. Dataset and Data Preprocessing

We validated our method on the datasets of ICPR 2012 andICPR 2014 challenge which are considered as the most famouscontest in mitosis detection. The team of Professor FrederiqueCapron, head of the Pathology Department at Pitie-SalppetriereHospital in Paris, France, has selected and annotated a setof breast cancer biopsy slides for generating the datasets. Weuse the slides which are stained with standard H&E dyes andscanned by scanners Aperio Scanscope XT. In each slide, wedetect the mitosis in frames at ˆ40 magnification. Accordingto the ICPR 2014 challenge, 1200 breast cancer histologyimages are used for training, while the remaining 496 imagesfor evaluation. The centroids of these mitoses are used asground truth. According to the ground truth, for FNR-model,we label the training patches as the following way: the patcheswhose centroids are closer than 10 pixels to the centroid of amitosis will be labeled as positive mitoses and the remainingregions are all labeled as negative non-mitoses. In addition,for FPR-model, we also extract patches whose centroids arecloser than 10 pixels to the centroid of a mimic (selected bythe doctors). To deal with the severe class imbalance betweenmitosis and non-mitosis, data augmentations(e.g., mirror, cropetc.) were conducted. After the augmentation, there are a totalof 106 non-mitoses patches, 3 ˆ 105 mitoses patches and3ˆ 105 hard mimics patches in the training set, respectively.

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TABLE IRESULTS ON THE ICPR 2012 DATASET

Method Precision Recall F-scoreUTRECHT [22] 0.511 0.680 0.584NEC [15] 0.747 0.590 0.659SUTECH 0.699 0.720 0.709IPAL [11] 0.698 0.740 0.718DNN [5] 0.886 0.700 0.782CasNN(average) [3] 0.804 0.772 0.788MSSN 0.776 0.787 0.781

TABLE IIRESULTS ON THE ICPR 2014 DATASET

Method Precision Recall F-scoreSTRASBOURG - - 0.024YILDIZ - - 0.167MINES-CURIE-INSERM - - 0.235CUHK - - 0.356CasNN(single) [3] 0.411 0.478 0.442CasNN(average) [3] 0.460 0.507 0.482MSSN 0.379 0.617 0.470

B. Evaluation Metric

Evaluation is performed according to the ICPR 2014 contestcriteria. A detected mitosis would be counted as correct if itscenter point is localized within a range of 8 µm of a groundtruth mitosis. we employ metrics including

Precision “TP

TP ` FPRecall “

TP

TP ` FN

F ´ score “2ˆRecall ˆ Precision

Recall ` Precision.

TP is the number of detected mitosis that are ground truthmitosis. FP is the number of mitosis that are not ground truth.FN is the number of ground truth mitosis that have not beendetected.

C. Detection Performance

Several typical detection examples are shown in Figure 6.The first column shows the results of one single sub-modelof the FNR-model (i.e., dilated convolution on 120 ˆ 120patches) and the second column shows the results of combinedresults of the FNR-model. The third column shows the resultsof the FPR-model (final detection results). The last columnshows the ground truth. According to the evaluation criteria ofMITOSIS challenges, we employed the following evaluationmetric including Recall, Precision, and F-score. The results canbe referred to Table I and Table II. Our detection results arethe best under the evaluation measurement of Recall, whichexceeds the ones of CasNN with about ten percentage. ForF-score, the overall evaluation measurement, our scores arecomparable to the state-of-art methods. In actually, we believethat the evaluation measurement Recall is more important thanPrecision in mitosis detection for clinical perspective.

D. False Negative Reduction Performance:

Here we evaluate the performance of the FNR-model. Weevaluated testing images in the ICPR 2014 dataset and counted

the number of FN the single sub-model and the combinedFNR-model generated respectively. We visualize the result inFigure 9. From Figure 9, we can see that the FNR-model canfurther reduce the FN from the single sub-model, which meansthat the combined FNR-model performs better than one singlescale sub-model.

-12 -10 -8 -6 -4 -2 0 2 4 6

-15

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-5

0

5

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Non-mitotic

Mimics

Fig. 7. Embedding the last layer of FPR-model.

Input patch

Layer 1

Layer 7, 8, 9

Last layer

Mitoses Mimics

Fig. 8. Visualization of different layers of FPR-model.

E. False Positive Reduction Performance:

We also evaluate the performance of the FPR-model. Wecounted the number of FP the FNR-model and the FPR-modelgenerated. We visualize the result in Figure 10 too. FromFigure 10, we can see that the FPR-model can further reducethe FP greatly. We also show the embedding from the lastlayer of the FPR-model in Figure 7. Three kinds of patchesare clearly separated, which means that a good performanceof reducing FP and FN could be expected. To better showthe performance of the FPR-model, we give a visualization ofdifferent layers of the FPR-model as shown in Figure 8. Threekinds of patches (i.e., mitoses, non-mitoses, mimics) can beeasily identified from the visualization.

V. CONCLUSION

Existing methods for mitosis detection either using thehandcrafted or deep features simply treat the detection taskas a (single stage) binary classification problem, which causesthe FN and FP are relative high. In this paper, we first analyzethe causes of the high FN and FP, and then present a novel two-stage multi-scale and similarity prediction network (MSSN).In MSSN, the first stage FNR-model fuses the outputs ofseveral sub-models to generate possible candidates, aiming toreduce FN. While in the second stage, based on the obtainedcandidates, FPR-model imposes a large margin constraint toreduce FP for getting the final detection results. We evaluate

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Fig. 9. The number of FN (ground truth that have not been detected) generated in the testing image.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15-10123

45678 FNR-model

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Fig. 10. The number of FP (detections that are not ground truth) generated in the testing image.

our MSSN on the ICPR 2014 and ICPR 2012 mitosis detectiondataset, which could be considered as the famous challengedataset in mitosis detection. Compared with the state-of-the-art models, our MSSN could obtain the highest recall valuesas well as a comparable F-score.

Acknowledgements: The work was supported by NSFCgrant (61432008, 61673203) and Young Elite Scientists Spon-sorship Program by CAST (2016QNRC001).

REFERENCES

[1] K. Alkuraya, P. Schraml, J. Torhorst, C. Tapia, B. Zaharieva, H. Novotny,H. Spichtin, R. Maurer, and Mirlacher. Prognostic relevance of geneamplifications and coamplifications in breast cancer. Cancer Research,64(23):8534, 2004. 1

[2] J. Bromley, I. Guyon, Y. Lecun, and R. Shah. Signature verification usinga ”siamese” time delay neural network. In International Conference onNeural Information Processing Systems, pages 737–744, 1993. 4

[3] H. Chen, Q. Dou, X. Wang, J. Qin, and P. A. Heng. Mitosis detection inbreast cancer histology images via deep cascaded networks. In ThirtiethAAAI Conference on Artificial Intelligence, pages 1160–1166, 2016. 2, 5

[4] S. Chopra, R. Hadsell, and Y. Lecun. Learning a similarity metricdiscriminatively, with application to face verification. In CVPR 2005.IEEE Computer Society Conference on, pages 539–546 vol. 1, 2005. 4

[5] D. C. Ciresan, A. Giusti, L. M. Gambardella, and J. Schmidhuber.Mitosis detection in breast cancer histology images with deep neuralnetworks. In MICCAI 2013 - 16th International Conference, Nagoya,Japan, September 22-26, 2013, Proceedings, Part II, pages 411–418,2013. 2, 3, 5

[6] M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman.The pascal visual object classes (voc) challenge. International Journalof Computer Vision, 88(2):303–338, 2010. 2, 3

[7] S. Gelfant. Initiation of mitosis in relation to the cell division cycle .Experimental Cell Research, 26(2):395–403, 1962. 1

[8] R. B. Girshick. Fast R-CNN. In ICCV 2015, Santiago, Chile, December7-13, 2015, pages 1440–1448, 2015. 2

[9] R. B. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich featurehierarchies for accurate object detection and semantic segmentation. InCVPR 2014, June 23-28, 2014, pages 580–587, 2014. 2

[10] E. Hoffer and N. Ailon. Deep metric learning using triplet network.In Similarity-Based Pattern Recognition - Third International Workshop,SIMBAD 2015, Copenhagen, Denmark, October 12-14, 2015, Proceed-ings, pages 84–92, 2015. 4

[11] H. Irshad. Automated mitosis detection in histopathology using mor-phological and multi-channel statistics features. Journal of PathologyInformatics, 4(1):10, 2013. 2, 5

[12] A. M. Khan, H. Eldaly, and N. M. Rajpoot. A gamma-gaussian mixturemodel for detection of mitotic cells in breast cancer histopathologyimages. Journal of Pathology Informatics, 4(4):11, 2013. 1, 2

[13] Y. Lecun and Y. Bengio. Convolutional networks for images, speech,and time series. MIT Press, 1998. 1

[14] J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks forsemantic segmentation. In CVPR 2015, Boston, MA, USA, June 7-12,2015, pages 3431–3440, 2015. 2

[15] C. D. Malon and C. Eric. Classification of mitotic figures withconvolutional neural networks and seeded blob features. Journal ofPathology Informatics, 4(1):9, 2013. 5

[16] J. Redmon, S. K. Divvala, R. B. Girshick, and A. Farhadi. You onlylook once: Unified, real-time object detection. In 2016 CVPR 2016, LasVegas, NV, USA, June 27-30, 2016, pages 779–788, 2016. 2

[17] S. Ren, K. He, R. B. Girshick, and J. Sun. Faster R-CNN: towardsreal-time object detection with region proposal networks. In Advances inNeural Information Processing Systems 28: Annual Conference on NeuralInformation Processing Systems 2015, December 7-12, 2015, Montreal,Quebec, Canada, pages 91–99, 2015. 2

[18] L. Roux, D. Racoceanu, N. Lomnie, M. Kulikova, H. Irshad, J. Klossa,F. Capron, C. Genestie, G. L. Naour, and M. N. Gurcan. Mitosis detectionin breast cancer histological images an icpr 2012 contest. Journal ofPathology Informatics, 4:8, 2013. 2

[19] C. Sommer, L. Fiaschi, F. A. Hamprecht, and D. W. Gerlich. Learning-based mitotic cell detection in histopathological images. In InternationalConference on Pattern Recognition, pages 2306–2309, 2012. 1, 2

[20] F. B. Tek. Mitosis detection using generic features and an ensemble ofcascade adaboosts. Journal of Pathology Informatics, 4(1):12, 2013. 2

[21] Welinder, Peter and Branson, Steve and Mita, Takeshi and Wah. Caltech-UCSD Birds 200. California Institute of Technology ,2010. 3

[22] M. Veta, P. J. V. Diest, and J. P. W. Pluim. Detecting mitotic figuresin breast cancer histopathology images. In SPIE Medical Imaging, page867607, 2013. 5

[23] H. Wang, A. Basavanhally, H. Gilmore, A. Madabhushi, A. Cruzroa,F. Gonzalez, N. Shih, M. Feldman, and J. Tomaszewski. Cascadedensemble of convolutional neural networks and handcrafted features formitosis detection. Proceedings of SPIE - The International Society forOptical Engineering, 9041(2):90410B–90410B–10, 2014. 1, 2, 3

[24] E. P. Xing, A. Y. Ng, M. I. Jordan, and S. J. Russell. Distance metriclearning with application to clustering with side-information. In Advancesin Neural Information Processing Systems 15 [Neural Information Pro-cessing Systems, NIPS 2002], pages 505–512, 2002. 2

[25] F. Yu and V. Koltun. Multi-scale context aggregation by dilatedconvolutions. CoRR, abs/1511.07122, 2015. 3

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