The Role of Machine Learning in Medical Data Analysis. A Case Study:

Flow Cytometry

Paolo Rota

, Florian Kleber

, Michael Reiter

, Stefanie Groeneveld-Krentz

and Martin Kampel

Computer Vision Lab (CVL), TU Wien, Vienna, Austria

Charit

e - Universitaetsmedizin, Berlin, Germany

Keywords:

Flow Cytometry, Leukemia (ALL), Deep Learning, Stacked Auto Encoders, GMM.

Abstract:

In last years automated medical data analysis turned out to be one of the frontiers of Machine Learning. Medi-

cal operators are still reluctant to rely completely in automated solutions at diagnosis stage. However, Machine

Learning researchers have focused their attention in this ﬁeld, proposing valuable methods having often an out-

come comparable to human evaluation. In this paper we give a brief overview on the role of Computer Vision

and Machine Learning in solving medical problems in an automatic (supervised or unsupervised) fashion, we

consider then a case study of Flow Cytometry data analysis for MRD assessment in Acute Lymphoblastic

Leukemia. The clinical evaluation procedure of this type of data consists in a time taking manual labeling that

can be performed only after an intensive training, however sometimes different experience may lead to dif-

ferent opinions. We are therefore proposing two different approaches: the ﬁrst is generative semi-supervised

Gaussian Mixture Model based approach, the latter is a discriminative semi-supervised Deep Learning based

approach.

1 INTRODUCTION

One of the recurrent questions is how Computer Vi-

sion and Machine Learning techniques are actually

making the difference in the daily routine. Learn-

ing based applications have been successfully em-

ployed for word and image search (Zheng et al.,

2015), semantic retrieval (Hofmanninger and Langs,

2015; Ramanathan et al., 2015), object classiﬁcation

(Gonzalez-Garcia et al., 2015) etc. These outstanding

results have contributed to increase the consciousness

of the potential of Machine Learning, directing the re-

searcher’s attention on other topics, targeting different

applications where the error is by far less tolerated.

These area of interest span from Video Surveillance to

Medical Applications, crossing Biometrics and Bioin-

formatics.

Medical data analysis is a topic where human su-

pervision has still a central position in every phase of

the process, from diagnosis to each stage of the treat-

ment. However research groups have focused their

attention on medical data analysis with the purpose

of automating different stages of the medical process

(Yoo et al., 2012).

Image based medical data analysis often relies

on Magnetic Resonance Imaging (MRI) or Positron

Emission Tomography (PET). In (Zhu et al., 2014)

the authors propose a joint regression and classiﬁca-

tion for Alzheimer’s disease and Mild Cognitive Im-

pairment diagnosis, analyzing the features in a novel

framework composed by similarity matrix and loss

sparse function reaching accuracy close to 100%. In

(Hofmanninger and Langs, 2015) they use medical

imaging in order to ﬁnd correspondences between im-

age segmentation and radiology reports bridging se-

mantics to medical data. However MRI and PET are

not the only possible source of information for high

standard medical data analysis. In (Qureshi et al.,

2014; Staal et al., 2004) the authors uses 2D color im-

ages of the retina in order to detect diabetes. In (Zhou

et al., 2014) the authors focus their attention on multi-

spectral images observed by the microscope in order

to perform cell classiﬁcation. Alternative technolo-

gies are used in order to extract important information

from cellular tissue, one of the most popular is the

Flow Cytometry (FCM), since it is a fast and cheap

methodology for cell analysis. FCM is a laser-based

biophysical technology that measures physical (size

and granularity) and biological (different cell types

can be detected with different markers) characteris-

tics of single cells in ﬂuid stream passing through a

laser beam. FCM is currently widely used by oncol-

Rota, P., Kleber, F., Reiter, M., Groeneveld-Krentz, S. and Kampel, M.

The Role of Machine Learning in Medical Data Analysis. A Case Study: Flow Cytometry.

DOI: 10.5220/0005675903030310

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 3: VISAPP, pages 305-312

ISBN: 978-989-758-175-5

305

Figure 1: Example of gating hierarchy used for manual minimal residual disease assessment in B-ALL.

ogists to manually detect remaining leukemic cells in

the bone marrow sample of a patient. Up to now anal-

ysis of FCM data is done manually.

In this paper we give an overview of the FCM

technique, describing the data and the acquisition

process, with particular interest to the Acute Lym-

phoblastic Leukaemia of type B (B-ALL) data. We

focus on the criticalities of the FCM data analysis,

examining the medical process highlighting the prob-

lems connected to the application of Machine Learn-

ing techniques on this type of data. The manual la-

beling strategy consists in a hierarchical procedure

named gating that strongly relies on the skills and ex-

pertise of the FCM operator. To overcome this sub-

jectivity issue we propose two automated, efﬁcient

and objective approaches to evaluate the FCM data;

the ﬁrst is supervised, based on deep learning and a

second generative semi-supervised based on Gaussian

Mixture Model (GMM). Minimal Residual Disease

(MRD)is the number of remaining leukemic cells at

certain time points during the treatment, allowing the

doctors to tailor therapy intensity according to the re-

sponse of each patient. The challenge of this method-

ology is that often certain cell populations are very

small compared to the overall sample size (sometimes

less than 0.1%).

The paper is structured as follows: in Sec. 2 is

described the FCM data acquisition procedure and

the way the assessment is performed by clinicians. In

Sec. 3 we outline the two proposed methods to assess

MRD in patients affected by B-ALL. In Sec. 4.1 we

give a brief description of the dataset used for the

evaluation stage which is described in Sec. 4. In Sec.

5 the results are presented and conclusions are drawn.

2 FLOW CYTOMETRY

In this section we describe brieﬂy the FCM data from

the acquisition to the MRD assessment in B-ALL.

In order to perform the acquisition, the bone mar-

row sample must be prepared, this procedure is called

staining and consists in adding a proper panel of con-

jugates (ﬂuorochrome/antibodies combination) to the

sample. The antibodies are ideally speciﬁcs to the

protein expression of a certain cell type. The ﬂuo-

rophores attached to the antibodies, are excited by the

laser beam of the Flow Cytometer. The stained cells

inside the sample are pushed through in a single ﬂow

and measured by lasers with different wavelengths.

Due to the possibility of noise, each measurement is

called event. The ﬂuorophores excitement is captured

by an electronic sensor. The device will also produce

physical measurement; the Forward Scatter (FSC, for

size measurement) and the Side Scatter (SSC, for

granularity measurement). A data value compensa-

tion is necessary due to the partial overlapping of the

ﬂuorescence spectra of the different ﬂuorochromes,

it is called spillover compensation and contributes in

building the statistical independence of the data. The

described procedure produces multiparameter read-

ings for each event present in the sample.

In order to ease the comprehension to not famil-

iar readers we propose a short description of the data

generated by the ﬂow cytometer:

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

306

Figure 2: A sample as it is generated by the ﬂow cytome-

ter. The ﬁgure is representing the Syto gate on the entire

set of cells in the 2D dimension composed by Syto 41 and

Forward Scatter parameters.

• Sample. A sample is the outcome of the mea-

surement of a stained bone marrow sample, at a

speciﬁc time point (an entire sample is shown in

Fig. 2).

• Event/Cell. Every measurement sensed by the

ﬂow cytomenter during the analysis of a sample.

Generally, an event refers to a cell. In Fig. 2 each

point is an event.

• Gate. The labeling phase is performed manu-

ally by experts, it consists in drawing polygons

on 2D scatter plots (see Fig. 2) following a hierar-

chical procedure (an example of gating hierarchy

used for MRD assessment in ALL is shown in Fig.

1.The main criticality of the gating procedure that

can affect the results is the operator subjectivity,

which is shown in clinical ring trials and present

deviations in MRD values (Dworzak et al., 2008).

• Blasts. The name blasts is referred to all the

cells that have been considered leukemic by ex-

pert. The medical assessment is therefore per-

formed counting the blasts events in relation to the

whole test sample (MRD value).

2.1 Related Works

In last few years there has been an increasing number

of approaches aiming at automating the FCM analysis

process (Bashashati and Brinkman, 2009; Aghaeep-

our et al., 2013). The main objectives of these algo-

rithms is to automatically assign each event to a spe-

ciﬁc biologically meaningful population, sometimes

relatively small (i.e. ten events out of two millions).

Unlike the manual gating, the automated methods per-

form the event clustering considering the whole mul-

tidimensional space at once. The outcome can be used

in the clinical routine or for further automatic inter-

pretation of the data. Most of the existing approaches

are unsupervised clustering methods adapted to be

very sensitive for small populations i.e. (Naim et al.,

2014). In this paper the authors propose a revised

GMM integrated with a splitting and merging proce-

dure that is particularly suitable to outline small bio-

logically meaningful populations. (Pyne et al., 2009)

is an EM-based multivariate ﬁnite mixture model al-

gorithm. The authors observed that the data clusters

are often skew and heavily-tailed, for this reason they

proposed a method that employs skew-t distributions.

In (Finak et al., 2009) the authors use an adapted ver-

sion of ﬂowClust (Lo et al., 2008) to identify cell sub-

populations, allowing the user to deﬁne the number of

distinct cell populations.

Regarding he automatic leukemic cell detection,

in the work proposed by Costa et al. (Costa et al.,

2010), the authors propose a supervised approach

where new events are classiﬁed using a nearest neigh-

bor classiﬁcation in the 2D-principal subspace, ob-

tained by principal component analysis of a labeled

training set. In (Toedling et al., 2006), the authors

propose a Support Vector Machines based frame-

work to automate leukemic cell detection in cytom-

etry where conventionally diagnosed data are used to

train the classiﬁers. As in (Toedling et al., 2006), our

interest is in classifying each event not only for dis-

criminating different populations but also to identify

that subset of events that corresponds to the blast pop-

ulation.

3 AUTOMATIC CELL

CLASSIFICATION

In this section we give a detailed description of the

two approaches proposed to estimate the MRD in

FCS data. The ﬁrst in Section 3.1 is fully discrimi-

native and based on deep architecture of Neural Net-

works, the second is a generative approach based on

the Gaussian Mixture Model and it is proposed in Sec-

tion 3.2.

3.1 Stacked Auto-encoders Approach

Recently, in the computer vision community, the Con-

volutional Neural Networks (CNN) have shown suc-

cess in many important tasks such as object recog-

nition (Zhang et al., 2015; Krizhevsky et al., 2012;

He et al., 2014), image segmentation (Cimpoi et al.,

2015; Hariharan et al., 2014), head pose estimation

(Conigliaro et al., 2015), to name a few. Although

this architectures are successfully used on images and

The Role of Machine Learning in Medical Data Analysis. A Case Study: Flow Cytometry

307

Input Layer Output LayerDecoder LayersEncoder Layers

1000 nodes

10 nodes

input

10 nodes

output

1000 nodes

500 nodes 500 nodes

250 nodes 250 nodes

30 nodes

(a)

Input Layer Encoder Layers

2 Fully-connected

Layers

non-blasts

blasts

1000 nodes

500 nodes

250 nodes

30 nodes

15 nodes

10 nodes

input

(b)

Figure 3: Scheme of the model adopted for the Stacked Auto-Encoders. On upper part (a) the unsupervised phase where the

ﬁrst 4 layer are learned directly from the data. In the sketch at the bottom (b) the supervised part of the network with two

extra fully connected layers in cascade to the encoders is shown.

videos, in FCM data this approach is not yet estab-

lished. In images, neighboring pixels are highly re-

lated to each other, this spacial property is not present

in FCM data. Parameters, in this domain, are indeed

standalone features concatenated without speciﬁc and

predeﬁned order in an one-dimensional array. The

combination of those features lead to the detection of

meaningful populations that are not always positioned

in the same location of the feature space.

In order to give a deep description of FCM data we

propose an approach based on a deep network based

on a Stacked layout of Auto-Encoders (SAE) (Ben-

gio, 2009; Vincent et al., 2010). This network, unlike

the CNN is more general and easily applicable to dif-

ferent type of data. There are two major reasons for

our choice: Firstly, this neural network, unlike CNN,

has more general purpose and it is easy to apply on

different types of data. The second reason is that this

type of neural network is extremely useful in discov-

ering interesting structure in the data (Bengio, 2009).

The proposed SAE architecture is composed by the

input layer of size 10x1, that is the number of param-

eters used to describe an event, this means that tis ap-

proach is event oriented, and we are trying to ﬁnd a

multi-dimensional hyperplane capable to separate the

two ﬁnal classes (details on the structure are noted in

Fig. 3). The training phase of the network consists of

two steps: ﬁrstly an unsupervised approach, in which

the network is forced to learn the data structure from

the training proposing a new interpretation of the in-

put features. The second is the supervised step, in

which the output of the net is forced to the labels in

order to adapt the weights to produce the ﬁnal infer-

ence.

3.2 Gaussian Mixture Model Approach

In our particular case, the cardinality of the dataset,

would make too computational demanding an ap-

proach based on kernel model estimation. A vi-

able solution is to estimate the model approximat-

ing the training set with a distribution generated by

a parametrized distribution. The Gaussian Mixture

Model (GMM) is widely used approach to fulﬁll this

task

. Because of its ﬂexibility in FCM data analy-

sis, in particular for population clustering (Naim et al.,

2014), the GMM leads to very good results. This gen-

erative approach has the ability of ﬁtting point cloud

distributions reducing the number of parameters com-

paring to a kernel based method.

In ALL data, as mentioned in Sec. 1, the blasts

population is often very small with respect to the

whole set of events. In order to avoid losing infor-

mation on the small populations and to semantically

give meaning to those distributions, we learned two

different models for blasts and non-blasts separately.

The estimation of such model is performed by a mod-

iﬁed Expectation Maximization (EM) algorithm sim-

ilar to (Naim et al., 2014). The two distributions are

generated by 10 and 2 gaussians for blasts and non-

blasts populations respectively. These values are the

empirical results of tests as an acceptable compromise

between accuracy and computation burden. The ﬁ-

nal model is the result of merging the two distribu-

tions by averaging their components. The resulting

distribution is shown in Fig. 4. Unlike (Naim et al.,

2014) our interest is not only in discriminating dif-

We remand to (Bishop, 2006) for the theoretical de-

scription of the model.

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

308

Figure 4: The result of the modeling performed by the GMM, we can distinguish in red the blasts population and in blue the

non-blasts.

ferent populations but also in the blasts identiﬁcation,

therefore the classiﬁcation of observations of a new

sample is done by a Bayes decision using the pos-

teriors obtained from the GMM components, where

the priors are set according to the average relative fre-

quencies of leukemic events in the training set. In Fig.

5 we can see a 2D projection of the samples with re-

spective gates drawn (A), and a GMM approximation

of the point distribution (B), the model of non blasts

4 EVALUATION

MRD is the relative frequency between the blasts cells

and the overall number of events in a sample as it is

stated in Eq. (1)

MRD

(i)

= N

(i)

blasts

(i)

events

(1)

where i refers to the i-th sample.

4.1 Dataset Description

In order to evaluate the performance of the algo-

rithms, we collected FCM-MRD measurements from

200 ALL patients treated according to the AIEOP-

BFM 2009 protocol. MRD was measured in bone

marrow samples of treatment day 15. Sample prepa-

ration and MRD assessment was performed following

the current international standard operating procedure

for 6color FCM-MRD detection. All FCM datasets

were gated manually by experienced operators using

a uniform gating procedure that is depicted in Fig. 1,

however, the parameters on which the blasts gate is

deﬁned may change among samples according to the

appearance of the sample and operator experience.

The FCM output provides for each individual cell

10 different parameters (three optical [FSC-A, FSC-

W, SSC-A] and seven ﬂuorescence based parame-

ters [CD20, CD10, CD45, CD34, SYTO 41, CD19,

CD38]). Each cell parameter becomes/represents a

dimension in the multidimensional data space.

For the evaluation the dataset has been divided in

two groups, training and test composed by 184 and

16 samples respectively. The division has been per-

formed randomly pooling the whole set of data. Vali-

dation for parameter tuning has been performed on a

random 30% of the training set. This operation has

been performed for 11 times in order to enlarge the

test set, resulting in a ﬁnal test set composed by 176

samples.

The Role of Machine Learning in Medical Data Analysis. A Case Study: Flow Cytometry

309

Figure 5: In row (A) is shown the gating as it has been performed manually by medical experts. In row (B) the GMM

components are sketched as they are trained by the EM process. In row (C) a representation of the PDF related to the non-

blasts population while in row (D) the model for blasts is shown along with the classiﬁed events.

4.2 Results

In order to fairly compare the approaches presented

in this paper, we show the results in two different

forms: graphical and numerical. The results proposed

in graphical form are scatter plots, where each point

represents one sample. The 2D coordinates of each

point are the values of true blasts in relation with the

predicted quantity. An ideal algorithm will produce a

scatter plot with samples disposed along the line y = x

(see Fig. 6).

In order to assess numerically the performance of

the algorithms we propose a comparison in terms of

mean square error (MSE) of the blast cells found in

each sample:

∑

(Blasts

− Blasts

)

(2)

where N

is the overall number of test samples, B

and B

are the number of true and predicted blasts in

the single sample. Numerical results are proposed in

Tab. 1

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

310

(a) (b)

Figure 6: Resulting graphs for the three approaches: (a) SAE and (c) the GMM based approach.

Table 1: The numerical results of the baseline compared to

the proposed method. The mean and variance are referred to

the absolute value of the difference between the automated

MRD and the true MRD.

Method MSE MSE Variance

SVM 0.01467 0.000637

SAE Deep Net-

work

0.00508 0.000121

GMM + Bayes

Decision

0.00891 0.000739

5 DISCUSSION

Performing an accurate MRD estimation in FCM data

with an automatic algorithm turns out to be a hard

undertaking, either using discriminative or generative

approaches. In the latter method we notice an impor-

tant tendency to underestimate the number of blasts in

the sample. This might be caused by the criticality in

ﬁnding a unique value for the component priors of the

model.

A critical drawback of the methods employed in

this work is that they construct ﬁx decision regions,

no adaptation is provided for new unseen and com-

plicated cases. Medical experts, use interpretation,

based on their expertise in order to draw the correct

gating around the blasts events, as result of several

consideration about the whole sample. Because of

this fact, the blasts gate can be drawn in a totally

different position with respect to a similar sample.

This leads to a non negligible error from an automatic

classiﬁer based on ﬁx decision. This last observation

stands in favor of the generative approach since it is,

unlike the Deep Network, sample oriented, while the

other two are event oriented. In this work the Deep

network are considering all training events as part of

an unique huge sample, this becomes a drawback dur-

ing the test phase since no sample structure is learned.

In all three cases however, the resolution of the algo-

rithms, in terms of accuracy at low MRD levels (be-

low 1000 events) is not sufﬁcient for the clinical rou-

tine.

In conclusion, as future work, we will consider an

extension of these approaches that, using the trained

model as a starting point, it will adapt to the new sam-

ple reﬁning the inference also in the most critical zone

of the graph.

ACKNOWLEDGEMENT

AutoFLOW project is supported by the European

Commission FP7-PEOPLE-2013-IAPP 610872. We

also gratefully acknowledge NVIDIA Corporation for

the donation of the Titan X GPU used for this re-

search.

REFERENCES

Aghaeepour, N., Finak, G., Hoos, H., Mosmann, T. R.,

Brinkman, R., Gottardo, R., Scheuermann, R. H.,

Consortium, F., Consortium, D., et al. (2013). Critical

assessment of automated ﬂow cytometry data analysis

techniques. Nature methods, 10(3):228–238.

Bashashati, A. and Brinkman, R. R. (2009). A survey of

ﬂow cytometry data analysis methods. Advances in

bioinformatics, 2009:584603–584603.

Bengio, Y. (2009). Learning deep architectures for ai. Ma-

chine Learning, 2(1):1–127.

Bishop, C. M. (2006). Pattern recognition and machine

learning. springer.

Cimpoi, M., Maji, S., and Vedaldi, A. (2015). Deep ﬁl-

ter banks for texture recognition and segmentation. In

Conference on Computer Vision and Pattern Recogni-

tion. IEEE.

Conigliaro, D., Rota, P., Setti, F., Bassetti, C., Conci, N.,

Sebe, N., and Cristani, M. (2015). The s-hock dataset:

Analyzing crowds at the stadium. In Conference on

Computer Vision and Pattern Recognition. IEEE.

The Role of Machine Learning in Medical Data Analysis. A Case Study: Flow Cytometry

311

Costa, E., Pedreira, C. E., Barrena, S., Lecrevisse, Q., Flo-

res, J., Quijano, S., Almeida, J., del Carmen Garcia-

Macias, M., Bottcher, S., Van Dongen, J., et al. (2010).

Automated pattern-guided principal component anal-

ysis vs expert-based immunophenotypic classiﬁcation

of b-cell chronic lymphoproliferative disorders: a step

forward in the standardization of clinical immunophe-

notyping. Leukemia, 24(11):1927–1933.

Dworzak, M. N., Gaipa, G., Ratei, R., Veltroni, M., Schu-

mich, A., Maglia, O., Karawajew, L., Benetello, A.,

otschger, U., Husak, Z., et al. (2008). Standardiza-

tion of ﬂow cytometric minimal residual disease eval-

uation in acute lymphoblastic leukemia: Multicentric

assessment is feasible. Cytometry Part B: Clinical Cy-

tometry, 74(6):331–340.

Finak, G., Bashashati, A., Brinkman, R., and Gottardo, R.

(2009). Merging mixture components for cell popu-

lation identiﬁcation in ﬂow cytometry. Advances in

Bioinformatics, 2009.

Gonzalez-Garcia, A., Vezhnevets, A., and Ferrari, V.

(2015). An active search strategy for efﬁcient object

class detection. In Conference on Computer Vision

and Pattern Recognition. IEEE.

Hariharan, B., Arbel

aez, P., Girshick, R., and Malik,

J. (2014). Hypercolumns for object segmenta-

tion and ﬁne-grained localization. arXiv preprint

arXiv:1411.5752.

He, K., Zhang, X., Ren, S., and Sun, J. (2014). Spatial pyra-

mid pooling in deep convolutional networks for visual

recognition. In European Conference of Computer Vi-

sion. Springer.

Hofmanninger, J. and Langs, G. (2015). Mapping visual

features to semantic proﬁles for retrieval in medical

imaging. In Conference on Computer Vision and Pat-

tern Recognition. IEEE.

Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Im-

agenet classiﬁcation with deep convolutional neural

networks. In Advances in neural information process-

ing systems, pages 1097–1105.

Lo, K., Brinkman, R. R., and Gottardo, R. (2008). Auto-

mated gating of ﬂow cytometry data via robust model-

based clustering. Cytometry Part A, 73(4):321–332.

Naim, I., Datta, S., Rebhahn, J., Cavenaugh, J. S., Mos-

mann, T. R., and Sharma, G. (2014). Swift - scal-

able clustering for automated identiﬁcation of rare cell

populations in large, high-dimensional ﬂow cytometry

datasets, part 1: Algorithm design. Cytometry Part A,

85(5):408–421.

Pyne, S., Hu, X., Wang, K., Rossin, E., Lin, T.-I.,

Maier, L. M., Baecher-Allan, C., McLachlan, G. J.,

Tamayo, P., Haﬂer, D. A., et al. (2009). Auto-

mated high-dimensional ﬂow cytometric data analy-

sis. Proceedings of the National Academy of Sciences,

106(21):8519–8524.

Qureshi, T. A., Hunter, A., and Al-Diri, B. (2014). A

bayesian framework for the local conﬁguration of reti-

nal junctions. In Conference on Computer Vision and

Pattern Recognition. IEEE.

Ramanathan, V., Li, C., Deng, J., Han, W., Li, Z., Gu, K.,

Song, Y., Bengio, S., Rossenberg, C., and Fei-Fei, L.

(2015). Learning semantic relationships for better ac-

tion retrieval in images. In Conference on Computer

Vision and Pattern Recognition. IEEE.

Staal, J., Abr

amoff, M. D., Niemeijer, M., Viergever, M.,

Van Ginneken, B., et al. (2004). Ridge-based vessel

segmentation in color images of the retina. Medical

Imaging, IEEE Transactions on, 23(4):501–509.

Toedling, J., Rhein, P., Ratei, R., Karawajew, L., and Spang,

R. (2006). Automated in-silico detection of cell popu-

lations in ﬂow cytometry readouts and its application

to leukemia disease monitoring. BMC bioinformatics,

7:282–282.

Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., and

Manzagol, P.-A. (2010). Stacked denoising autoen-

coders: Learning useful representations in a deep net-

work with a local denoising criterion. The Journal of

Machine Learning Research, 11:3371–3408.

Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K.,

Gopidi, R., Chang, J.-F., and Hua, L. (2012). Data

mining in healthcare and biomedicine: a survey of the

literature. Journal of medical systems, 36(4):2431–

2448.

Zhang, Y., Sohn, K., Villegas, R., Pan, G., and Lee, H.

(2015). Improving object detection with deep convo-

lutional networks via bayesian optimization and struc-

tured prediction. In Conference on Computer Vision

and Pattern Recognition. IEEE.

Zheng, L., Wang, S., Tian, L., He, F., Liu, Z., and Tian, Q.

(2015). Query-adaptive late fusion for image search

and person re-identiﬁcation. In Conference on Com-

puter Vision and Pattern Recognition. IEEE.

Zhou, Y., Chang, H., Barner, K., Spellman, P., and Parvin,

B. (2014). Classiﬁcation of histology sections via

multispectral convolutional sparse coding. In Con-

ference on Computer Vision and Pattern Recognition.

IEEE.

Zhu, X., Suk, H.-I., and Shen, D. (2014). Matrix-

similarity based loss function and feature selection for

alzheimer’s disease diagnosis. In Conference on Com-

puter Vision and Pattern Recognition. IEEE.

VISAPP 2016 - International Conference on Computer Vision Theory and Applications

312