CNN based Mitotic HEp-2 Cell Image Detection

Krati Gupta, Arnav Bhavsar and Anil K. Sao

School of Computing & Electrical Engineering, Indian Institute of Technology Mandi, Mandi (HP), India

Keywords:

Mitotic Cells, HEp-2 cells, Auto-immune Disorders, Computer-Aided Detection (CAD), CNN Architecture,

Support Vector Machines, AlexNet Architecture.

Abstract:

We propose a Convolutional Neural Network (CNN) framework to detect the individual mitotic HEp-2 cells

against non-mitotic cells, which is important for Computer-Aided Detection (CAD) system for auto-immune

disease diagnosis. The signiﬁcant aspect of detecting mitotic HEp-2 cells is to consider the distinctive appear-

ance differences between the mitotic and non-mitotic classes that are represented through the learned features

from pre-trained CNN. We especially focus on gauging the effectiveness of learned features from different

CNN layers, combined with traditional Support Vector Machine (SVM) classiﬁer. We also consider the class

sample skew between the classes. Importantly, we compare and discuss the performance of learned feature

representations, and show that some of these features are indeed very effective in discriminating mitotic and

non-mitotic cells. We demonstrate a high classiﬁcation performance using the proposed framework.

1 INTRODUCTION

Indirect ImmunoFluorescence (IIF) imaging is con-

sidered to be a ‘gold standard’ to characterize the

presence of Anti-Nuclear Antibody (ANA), in order

to diagnose a connective tissue disorder i.e. autoim-

mune disorders (Kumar Y, 2009; Foggia et al., 2013;

Hobson et al., 2016). Here HEp-2 cells are used as

substrate, in which different antibodies on cells are

visualized in form of distinct nuclear staining pat-

terns in images and can be utilized to develop an ef-

ﬁcient CAD system for aiding diagnosis. The promi-

nent nuclear staining patterns show some signiﬁcance

in detection of staining patterns-speciﬁc antigens and

the associated diseases. Due to subjective & semi-

quantitative procedure, manual handling errors, intra-

personnel and laboratory variations (Foggia et al.,

2013; Hobson et al., 2014), the manual protocol of

detection requires to be aided with a CAD system

that can efﬁciently identify and detect the presence of

staining patterns and associated diseases, in order to

aid pathologists for obvious and non-ambiguous cases

and reduce their work pressure.

The staining patterns generated on HEp-2 is based

on the bonding between auto-antibodies to the cell

components at different stages of the cell cycle. The

staining patterns in a cell are visualized mainly in two

cell cycle stages: interphase and mitosis stages (Tonti

et al., 2015). As yet, most automated approaches have

explored the classiﬁcation schemes on dominant pat-

terns of interphase stage (Hobson et al., 2016; Gupta

et al., 2016; Gupta et al., 2014; Manivannan et al.,

2016). However, the detection of mitotic type cells is

also an important and principal step in HEp-2 screen-

ing framework (Miros et al., 2015) as the antigens

released by the mitotic cells and their concentration

are responsible for some lethal diseases. Also the

identiﬁcation of mitotic pattern cells is a beneﬁcial

indication for narrowing down the patient cell pat-

terns. According to the literature, it is noticed that the

mitotic cell detection problem can be addressed us-

ing the secondary counter-stain during staining pro-

cedure, which is not an economical process. Thus,

in the present work, we primarily focus on the prob-

lem of mitotic cell detection, using only the primary

counter-stain, which is itself a novel problem deﬁ-

nition. Moreover, due to rare occurrence of mitotic

samples, the sample imbalance between the mitotic

and non-mitotic class during classiﬁcation is also an

important concern in this case. Therefore, for a com-

plete screening system, it is crucial to consider mitotic

phase cells also.

To the best of our knowledge, few authors have an-

alyzed such mitotic cell detection problem for HEp-2

cases. For instance, in (Iannello et al., 2014; Fog-

gia et al., 2010), authors have proposed a mitotic v/s

interphase/non-mitotic cell classiﬁcation criteria on

a very less number of samples using morphological

Gupta, K., Bhavsar, A. and Sao, A.

CNN based Mitotic HEp-2 Cell Image Detection.

DOI: 10.5220/0006721501670174

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 2: BIOIMAGING, pages 167-174

ISBN: 978-989-758-278-3

167

(a) (b) (c) (d) (e) (f) (g)

Figure 1: Some examples of cell images (a) to (c) mitotic cells & (d) to (g) non-mitotic cells.

and textural feature representation. In (Percannella

et al., 2011), the authors have addressed the chal-

lenge of class sample skew or imbalance and have

reviewed methods to handle the same. However,

these approaches do not consider any standard pub-

licly available dataset. In this work, we evaluate our

approach using publicly available standard dataset,

which would set a benchmark and help in compara-

tive analysis of future studies.

In the proposed work, we focus on the classiﬁca-

tion of mitotic v/s non-mitotic cell images using fea-

tures learned by a CNN. In current scenario of pattern

recognition based works, CNNs have been proved to

be efﬁcient and reliable models to achieve remark-

able performance for image classiﬁcation and object

detection tasks. Moreover, pre-trained CNN archi-

tectures can also perform an important role in terms

of feature extractors. Hence, as a part of the work,

we also analyze the effectiveness of features learned

from different layers of the CNN. As we focus on con-

sidering the effectiveness of the features (and not on

using a complex non-linear classiﬁcation model), we

use the features in a standard linear SVM, which is

a popular linear classiﬁer. In current work, we ex-

tract features from the layers of a pre-trained AlexNet

architecture (Krizhevsky et al., 2012). The main con-

tributions of the work are as follows:

(I) Generally, a visual clear distinction can be ob-

served between the mitotic and non-mitotic classes.

Such distinction can be seen in few examples of mi-

totic and non-mitotic cells, shown in Figure 1. Fig-

ures 1(a) to 1(c) are mitotic cells, while the remaining

cells are non-mitotic. Considering this, we demon-

strate that the learned feature representation through

CNN layers can be effective in capturing such a dis-

tinction between the two types of cells, even when

using the features from a CNN which is pre-trained

on scene/object images. In this work, for the feature

extraction task, we use the pre-trained AlexNet archi-

tecture.

(II) We also focus on an important aspect of the

problem, i.e. class sample skew between the mitotic

v/s non-mitotic classes, wherein the mitotic (i.e. the

positive class) samples are very less in number, than

non-mitotic ones. Hence, we apply two standard data

skew handling strategies: undersampling and over-

sampling, and draw some useful insights.

(III) In addition to classiﬁcation, we experimen-

tally analyze the effectiveness of features learned at

different CNN layers (low-level, mid-level and high-

level features), and also provide a (standard) low-

dimensional visual representation to support the ex-

perimental results.

Figure 2: Block diagram of the traditional approach v/s

CNN based classiﬁcation approach.

(IV) In Figure 2, we have shown the block di-

agram of both a conventional and a CNN based

classiﬁcation approach. Traditional classiﬁcation

approaches typically involve steps such as pre-

processing, segmentation and feature extraction task.

Distortions in any of these tasks can accumulate and

lead to inaccuracies. The proposed application of

CNN is independent of pre-processing and segmen-

tation. Here, the entire cell image is treated as in-

put, while in case of traditional feature extraction ap-

proaches, the feature representation task is highly de-

pendent on the selection of an appropriate Region-

of-Interest (ROI) (e.g. (Iannello et al., 2014; Foggia

et al., 2010)). Hence, such a work also demonstrates

the usefulness of segmentation-free classiﬁcation for

cell images.

2 PROPOSED APPROACH

As mentioned earlier, we use the learned feature rep-

resentation from CNN architecture for classiﬁcation

of mitotic v/s non-mitotic class samples and analyz-

ing the effect of feature representation extracted from

different layers of CNN. More speciﬁcally, the fea-

ture responses used in this work are from the conv1,

conv2, conv3, conv4, conv5, and pool5 layers of a

pre-trained AlexNet architecture. Thus we extract

the feature representation of individual images from

a CNN and then apply the same features in SVM,

in order to get the better discrimination between the

classes.

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168

Figure 3: AlexNet architecture.

We now elaborate on the important aspects of our

proposed approach.

2.1 Pre-trained CNN Architecture

Unlike the prior approaches for the mitotic vs non-

mitotic classiﬁcation task, we propose to use a CNN

based feature representation scheme, in which the

output responses of each layer are used as a generic

feature representation. The idea of using CNN based

features is motivated by its efﬁcacy in various image

processing and pattern recognition tasks (Xu et al.,

2015; Sharif Razavian et al., 2014). We have used a

publicly available pre-trained CNN architecture, i.e.

AlexNet (Krizhevsky et al., 2012), trained on 1.3

million high-resolution images in the LSVRC-2010

ImageNet training set into the 1000 different object

classes. Figure 3 shows the structure of the network.

The network consists of 8 learned layers, including

5 convolutional layers with a kernel size varies from

3 × 3 to 11 × 11 and 3 fully connected layers. Recti-

ﬁed Linear Units (ReLU) is used as a non-linear acti-

vation function at each layer. Maxpooling kernels of

size 3 × 3 are used at the different layers to build ro-

bustness for intra-class variations. We have used the

pre-trained CNN architecture, that is trained on ob-

ject images. Considering the 2D gray level images

involved in the work, we use the 2D CNN. However,

the proposed framework (including the CNN used) is

not restricted to the input image dimension and can

be extended in a straightforward manner for 3D input

images also.

Also involving the visual differences in the two

classes, we believe that the low-level or the higher-

level features representation from such a pre-trained

CNN can still effectively capture the discriminative

information from cell images also. In section 3, we

provide and discuss some activation maps to elaborate

on this point, based on visualization of the feature rep-

resentation. In Table 1, the details of layer types are

given, along with the ﬁlter sizes and stride rates.

Table 1: Details in pre-trained AlexNet architecture.

layer Type Size

1 conv1 layer + ReLu 96 × 55 × 55

2 maxpool1 96 × 27 × 27, stride 2

3 conv2 layer + Relu 256 × 27 × 27

4 maxpool2 256 × 13 × 13, stride 2

5 conv3 layer + ReLu 384 × 13 × 13

6 conv4 layer + ReLu 384 × 13 × 13

7 conv5 layer + ReLu 256 × 13 × 13

8 maxpool5 256 × 6 × 6, stride 2

2.2 Layer-wise Features

Importantly, we consider the extraction of the output

responses from different layers of CNN as low-level

features, mid-level features and high level features

representation. Hence, we use the output responses

of layer conv1 with 96×55 × 55 & conv2 with 256 ×

27 × 27 response as low-level feature representation,

conv3 with 384 × 13 × 13 as mid-level feature repre-

sentation and similarly conv4 with 384 × 13 × 13 &

conv5 with 256 × 13 × 13 output responses as high-

level feature representation. Thus, we explore the lay-

ers for which we get a good representation for dis-

criminating the input samples.

2.3 Classiﬁcation Task

For classiﬁcation task, we employ the traditional clas-

siﬁer i.e. Support Vector Machines (SVM). The SVM

classiﬁer can select very few optimal samples (the

support vectors) to build the ﬁnal model. Arguably,

this aspect can be important in a scenario where we

have less data (as is the case for the undersampling

scenario in this work). Thus, we believe that the SVM

can be a good choice of classiﬁer, as we also later

demonstrate. For SVM classiﬁer, we chose the stan-

dard linear kernel instead of using any non-linear ker-

nel. This is because of one of the purposes of this

work is to gauge the effectiveness of different lay-

ers of CNN features, and a linear classiﬁer would not

transform features in any manner. Indeed, a linear

CNN based Mitotic HEp-2 Cell Image Detection

169

(a) (b) (c)

(d) (e)

Figure 4: 2-D t-SNE-plot of high dimensional feature representation at (a) conv1, (b) conv2, (c) conv3, (d) conv4, and (e)

conv5 (Better viewed in color or by zooming the images). The high dimensional feature representation is demonstrated in

) space.

separation in features is indicated by the t-SNE plots

(Van der Maaten and Hinton, 2008) of Figure 4 .

For comparison, we also perform classiﬁcation us-

ing the same CNN architecture with Fully Connected

(FC) layers. We treat this as a baseline classiﬁer, and

draw some useful insight from this comparison.

2.4 Data-skew

Considering a large amount of data skew between

both the classes, we apply a well-known data skew

handling techniques of undersampling and oversam-

pling (for the case of the baseline CNN classiﬁer).

In the ﬁrst case, we undersample the majority class

samples in data space itself. Here, mitotic class im-

ages are the positive images with minority and non-

mitotic is the majority class. Undersampling is done

by randomly removing few samples from the non-

mitotic class and equalizing the samples with the mi-

nority class. We demonstrate that most of our results

are good even with undersampling. In the second

case, an oversampling technique has also been tried

for the same case, where oversampling of minority

class samples are done using data augmentation with

rotation. We rotate images in a range of angles be-

tween 0

◦

to 150

◦

in data space itself. In this case, the

oversampling of minority class is done 40 times.

3 EXPERIMENTS & RESULTS

In this section, we describe about the dataset, experi-

mental details, and results of the proposed approach.

3.1 Dataset Description

To validate our approach, we have used a publicly

available dataset, i.e I3A Task 3 mitotic cell detection

dataset (Vento, ; Foggia et al., 2013). It comprises of

100 mitotic cells and 4228 non-mitotic cells. All cells

are accompanied with a ground truth or mask images.

The mask images are provided to locate the cell nu-

clei region, though we have not used the mask image

in our current work. The mitotic images are annotated

as +1 and non-mitotic mask images as -1.

3.2 Experiment Details

For evaluation, we report the True-Positives (TP) and

False-Positives (FP) accuracy. Mitotic class is con-

sidered as the positive class here. The F-score is also

calculated for the results, which is the harmonic mean

of precision and recall. Here, precision is deﬁned

T P

T P+FP

and recall is

T P

T P+FN

and FN denotes False

Negatives.

We report results on a series of experiments that

we conducted on different sets of training, testing and

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170

Table 2: Experimental results of mitotic v/s non-mitotic classiﬁcation. Here US & OS stand for undersampling and oversam-

pling results respectively.

Layers Training set Validation set Testing set TP acc. FP acc. Precision Recall F-score

(%) (%) (%) (%) (%)

Conv1

30 10 60 87.33 1.23 0.98 0.87 0.92

40 10 50 90.60 1.14 0.98 0.90 0.94

50 10 40 91.50 1.33 0.99 0.91 0.96

Conv2

30 10 60 90.17 1.13 0.99 0.90 0.94

40 10 50 94.80 1.20 0.99 0.95 0.96

50 10 40 95.00 1.24 0.99 0.95 0.97

Conv3

30 10 60 25.90 2.41 0.92 0.26 0.41

40 10 50 35.60 3.62 0.91 0.36 0.52

50 10 40 37.31 3.81 0.91 0.37 0.52

Conv4

30 10 60 98.33 1.14 0.99 0.98 0.98

40 10 50 100 1.07 0.98 1.00 0.99

50 10 40 100 1.08 0.99 1.00 0.99

Conv5

30 10 60 93.6 1.21 0.98 0.93 0.96

40 10 50 98.8 1.21 0.98 0.98 0.98

50 10 40 100 1.21 1.00 1.00 0.99

Pool5

30 10 60 93.60 1.10 0.98 0.93 0.96

40 10 50 99.8 1.19 0.98 0.99 0.98

50 10 40 100 1.10 0.99 1.00 0.99

Baseline-US

30 10 60 68.33 1.02 0.99 0.68 0.81

40 10 50 76 1.04 0.99 0.76 0.8

50 10 40 92.5 1.05 0.99 0.93 0.95

Baseline-OS

30 10 60 95 1.4 0.99 0.95 0.97

40 10 50 98 1.14 0.99 0.98 0.98

50 10 40 80 1.1 0.99 0.8 0.88

validation sets. The dataset is divided into different

ratio of training, validation and testing sets that is

clearly described in Table 2. In our experimentation,

the entire dataset is divided into 3 experimental sets

of 50%, 40% and 30% training sets and 40%, 50%

and 60% testing sets respectively. The average re-

sults are reported over 10 random trials to maintain

the robustness of the experimentation. In this way,

each sample will once include in training, validation

or testing set. In all experimentations, we have used

10% data samples for validation sets, in order to chose

the best model and parameters settings. For classiﬁ-

cation, SVM classiﬁer with standard linear kernel is

used. For this, we have used the well known and efﬁ-

cient LIBLINEAR toolkit (Fan et al., 2008) with best

chosen parameters.

3.3 Results

We report the TP, FP accuracy, precision, recall and

F-score of all experiments in Table 2. The results are

reported in form of low-level, mid and high level fea-

ture representation. As the networks is deep, so we

consider the features from layers 4 and 5 as higher

level features, those from layers 1 and 2 as lower level

features and those from layer 3 as mid-level.

To visually show the discrimination between the

classes and support our experimental results, t-SNE

plots of higher dimensional feature representation of

all the experiments are also presented in 2-dimensions

(better viewed in color) in Figure 4. t-SNE is an

approach to represent high dimensional data in 2-

dimensions (Van der Maaten and Hinton, 2008).

Moreover, we also visualize the activation maps,

i.e. feature maps of conv1 as low-level features,

conv3 as mid-level features and conv5 as high-level

feature representation in Figure 5 (Zeiler and Fergus,

2014), by using a deep visualization toolbox (Yosin-

ski et al., 2015). The feature maps are shown for mi-

totic as well as for one non-mitotic cell class.

We observe that the classiﬁcation accuracies of

low-level feature representation and high-level repre-

sentation are very high, even with very low amount of

data due to undersampling (which obviates the need

for oversampling in this case). The F-score values are

more than 0.9 in most of the cases. On the other hand,

the mid-level features show a very low accuracy sug-

gesting that these are not discriminative enough.

We discuss more about the layer-wise perfor-

mance along with some observations from the t-SNE

plots and activation maps, and the comparison with

the baseline CNN classiﬁer, in the following subsec-

tions.

CNN based Mitotic HEp-2 Cell Image Detection

171

Figure 5: CNN-based feature extraction. Activation maps from Conv1 layer as low-level feature representation (2nd column),

Conv3 as mid-level (3rd column) and Conv5 as high-level feature representation (4th column) are shown.

3.3.1 Conv1 and Conv2: Low-level Feature

Representation

For low-level feature description, we extract the

learned ﬁlter responses from conv1. Typically, the

low-level representations exploits the intensity-based

and some texture-based discriminative information.

One can note that the classiﬁcation results using

conv1 and conv2 features are quite good. This indi-

cates that the low-level intensity variation and texture

information (as also observed in Figure. 1 images)

can be effective for classiﬁcation. The clear discrimi-

nation between samples is also observed in the t-SNE

plot (Figures. 4(a) and 4(b)).

On the basis of visualization of activation maps

examples from Figure 5 and classiﬁcation results

from Table 2, we observe that low-level feature rep-

resentation are indeed clearly discriminative and rep-

resent the characteristics well. This is because the

low-level features such as lines, edges, corners and

dots or small blobs etc. are typically common to im-

ages irrespective of their domain. Thus, in this case,

if there is discriminative information at the low-level

such features are able to capture the same, even if the

network is trained on object image data. Hence, we

conclude that the AlexNet architecture, pre-trained on

object images shows the feature representation, in-

cluding the combinations of basic low-level entities

in different sizes, shapes and textures, which are also

quite useful for our classiﬁcation task.

3.3.2 Conv3: Mid-level Feature Representation

We note the classiﬁcation accuracies with conv3 fea-

tures are quite low. As implied in some reported

works (Oquab et al., 2014), the mid-level layer may

require some re-training with respect to the cell im-

ages, as the pre-trained architecture is trained on a

very different domain of images. Hence, the con-

textual information and feature representation is quite

different and not discriminative for this domain of im-

ages.

We also note that we are employing a linear classi-

ﬁer, and as observed in the t-SNE plot (Figures. 4(c)),

these features are not linearly separated, and may

need non-linear models for better classiﬁcation. In

Figure 5, the activation maps of the feature responses

from conv3 are shown. We observe that the features

from conv3 across classes do not differ as clearly as

in the case of low-level features. Thus, many ﬁlter

may have irrelevant information, which may reduce

the performance of classiﬁer. Having said that, it is

worth further exploring the interpretation of the mid-

level features in this context.

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3.3.3 Conv4, Conv5 and Pool5: High-level

Feature Representation

With regards to the discussion above, this is an inter-

esting case. We observe that the classiﬁcation accura-

cies with learned features from conv4 and conv5 lay-

ers are very high. As for the low-level features, in this

case too, the t-SNE plot (Figures. 4(d) and 4(e)) show

a clear linear discrimination between the two classes.

Typically, these feature representation is known

to incorporate more semantic information for object

/ scene images. However, for the cell image classiﬁ-

cation case, the images do not seem to have such high

level semantic information, and the activation maps in

Figure 5 seem to support this argument. Thus, while

the activation maps are again not as clearly represen-

tative as those for low-level features, one hypothesis

for the high performance is that the activation maps

are also highly sparse. This could indicate that ﬁlter

responses of irrelevant ﬁlters (in conv3) might be sup-

pressed in high-level features and only few relevant

ﬁlters might activated with discriminative responses.

Again, as for conv3 responses, this is also worth ex-

ploring further to better analyze and interpret the per-

formance of high-level features in this case.

3.4 Comparison with the Baseline CNN

Classiﬁer

Finally, we compare with the baseline CNN classiﬁer

wherein fully connected (FC) layers in the Alexnet

are retrained for classiﬁcation (Table 2). Note that

both the baseline classiﬁer and one case with the

SVM classiﬁer, operate on the pool5 features. In-

terestingly, in the undersampled case, the baseline

classiﬁer shows a much lower performance than the

SVM classiﬁer, especially with low-amount of train-

ing data. We believe that this is due to over-ﬁtting,

as the amount of data is low. As indicated earlier, the

SVM classiﬁer could be more robust here, as it effec-

tively models the classiﬁer using less number of sam-

ples. Typically, in many applications, the CNN clas-

siﬁer is trained with oversampled data. In the over-

sampled case, as the data size increases, the baseline

CNN learns better and the accuracy increases. How-

ever, the note that the SVM operating on undersam-

pled data performs equally well.

To the best of our knowledge, there are very few

approaches proposed for this task, and indeed this

is the ﬁrst work on this dataset. Hence, we do not

provide any comparisons with any other approaches.

However, even in an absolute sense the best results

achieved in this work are quite high and demonstrate

that the approach is very effective to discriminate the

mitotic and non-mitotic class samples.

4 CONCLUSION

In the proposed work, a mitotic cell detection frame-

work for HEp-2 cell images is proposed via learned

feature representation with a pre-trained CNN. We

achieve high quality performance with low-level and

high-level layered features of the architecture. Fur-

thermore, we discuss some useful observations with

respect to the features at various levels, and compari-

son with a baseline CNN. In future, we mean to build

our own classiﬁcation CNN architecture or re-train

selected layers (transfer learning), which may also

help in achieving better insights.

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