Enhanced Deep Learning for Pathology Image Classification:

A Knowledge Transfer based Stepwise Fine-tuning Scheme

Jia Qu

, Nobuyuki Hiruta

, Kensuke Terai

, Hirokazu Nosato

Masahiro Murakawa

1,3

and Hidenori Sakanashi

1,3

Department of Intelligent Interaction Technologies, University of Tsukuba, Tsukuba, Japan

Department of Surgical Pathology, Toho University Sakura Medical Center, Sakura, Japan

Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Japan

Keywords: Pathology Image, Deep Learning, Transfer Learning, Color-Index Local Auto-Correlation (CILAC).

Abstract: Deep learning using Convolutional Neural Networks (CNN) has been demonstrated unprecedentedly

powerful for image classification. Subsequently, computer-aided diagnosis (CAD) for pathology image has

been largely facilitated due to the deep learning related approaches. However, because of extremely high

cost of pathologist's professional work, the lack of well annotated pathological image data to train deep

neural networks is currently a big problem. Aiming at further improving the performance of deep neural

networks and alleviating the lack of annotated pathology data, we propose a full-automatic knowledge

transferring based stepwise fine-tuning scheme to make deep neural networks follow pathologist’s

perception manner and understand pathology step by step. To realize this conception, we also introduce a

new type of target correlation intermediate dataset which can be yielded by using fully automated

processing. By extracting rough but stain-robust pathology-related information from unannotated pathology

images with handcrafted features, and making use of these materials to intermediately train deep neural

networks, deep neural networks are expected to acquire fundamental pathological knowledge in advance so

that boosted in the final task. In experiments, we validate the new scheme on several well-known deep

neural networks. Correspondingly, the results present solid evidence for the effectiveness and suggest

feasibility for other tasks.

1 INTRODUCTION

Cancer is one of the most terrible threats to human

health. According to the data (Ferlay J. et al., 2013),

there were approximately 14.1 million new cancer

cases and 8.2 million deaths worldwide in 2012.

Moreover, same report estimates that the number of

new cancer cases may increase to 24 million by

2035. Nowadays, we have many advanced cancer

diagnosis modalities such as Computed Tomography

(CT), Magnetic Resonance Imaging (MRI), and

Positron Emission Tomography (PET). Meanwhile,

pathology image diagnosis is still playing a key role

to assess cancer’s presence or absence. However, the

shortage of pathologists has become a conspicuous

problem in many countries. In japan, the number of

pathologists per 100,000 people is 1.95, which is

around only 1/3 of that in the United States (M.

Fukayama et al. 2015). In China, this number is even

as low as 1.35 (Cornish, 2014). The severe shortage

directly results high workload of pathologists and

increasing misjudgement in diagnosis. Although

digital pathology has widely popularized since more

than a decade ago, confirmation of a mass of large-

scale images remains heavy load to pathologists.

With respect to this issue, efforts on automatic

diagnosis of pathology images based on pattern

recognition technology are regarded as one the most

promising solution.

2 RELATED WORKS

In early periods, researchers used to adopt

conventional image classification approaches based

on pathological morphology indexes (e.g. nuclei-

cytoplasmic ratio and density) and generalized

texture descriptors to map the images to feature

spaces for further modelling. Compared to the

former approaches, the latter ones have shown more

Qu, J., Hiruta, N., Terai, K., Nosato, H., Murakawa, M. and Sakanashi, H.

Enhanced Deep Learning for Pathology Image Classiﬁcation: A Knowledge Transfer based Stepwise Fine-tuning Scheme.

DOI: 10.5220/0007356100920099

In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019), pages 92-99

ISBN: 978-989-758-353-7

robustness to the ever-changing cancerous

appearance. Esgiar et al. (Esgiar et al., 1998)

employed GLCM to calculate the contrast, entropy,

angular second moment, dissimilarity and

correlation from colon’s pathology images, and used

linear discriminate analysis (LDA) and k-nearest

neighbour algorithm (KNN) to distinguish normal

and cancerous images. J. Diamond et al. (J.

Diamond et al., 2004) employed an evolved version

of GLCM, called Haralick features to classify

prostate pathology images. In Masood’s study (K.

Masood and N. Rajpoot, 2009), local binary pattern

(LBP) and support vector machines (SVM) are

utilized and demonstrated effectiveness for colon

pathology images. Besides, lower-order and higher-

order histogram features, Gabor filters and

Perception-like features are involved in pathology

image classification as well. However, when using

the generalized texture features, researches have

faced a common problem: it is very hard to control

the adaptability and select the serviceable one or part

(Shen et al., 2017). Meanwhile, the non-uniform

staining quality among data resources and other

changing factors makes the classification more

challenging (R. Marée, 2017, Chen et al., 2016, B.

Bejnordi et al., 2016).

In recent years, deep learning using

convolutional neural networks (CNN) (A.

Krizhevsky et al., 2012) has shown its

unprecedented capacity to defuse these problems.

Due to more domain agnostic approach combining

both feature discovery and implementation to

maximally discriminate between the classes of

interest (Janowczyk and Madabhushi, 2016), high

hope has been placed on deep learning to accelerate

classification of pathology image (Xu et al., 2017,

Hou et al., 2016, Xu et al., 2016). When one adopts

deep learning based approaches, large datasets are

always indispensable to train more capable deep

neural networks and raise the performance.

However, unlike natural image datasets which can

be acquired based on internet and automated

categorizing techniques, building up high quality

pathology image datasets, anyhow, requires

professional observation and annotation by

pathologists. Because of the necessity of this

procedure, well-annotated data usually cost vast

financial resources and manpower. In this situation,

drawing out the maximum power of deep neural

networks with limited datasets has become a very

important practical issue.

3 STEPWISE FINE-TUNING FOR

DEEP NEURAL NETWORKS

When holding a certain amount of data, fine-tuning

the deep neural networks is one of the evidenced

techniques able to boost the performance in some

degree. Rather than training from scratch, fine-

tuning a general neural network which has been pre-

trained with large-scale image datasets (e.g.

ImageNet) to obtain a more specialized network

corresponding to target tasks can usually yield more

advantageous results (Chen et al., 2015, Shin et al.,

2016, Yosinski et al., 2014). Training a CNN

strongly depend on its initial status, thus it is

significant to obtain appropriate initialization as

much as possible in order to avoid over-fitted

learning or local minimum traps. Generally, the

forepart layers of a CNN are considered analogous

to the conventional texture features and applicable to

many of related tasks, while the later layers capture

more abstract image content by combining low-layer

features involving more specific information

corresponding to the target task (Brachmann et al.,

2017). Based on this fact, if the tasks of pre-training

and final classification are sufficiently correlated

(for instance, both of them are for color image

classification), one may only fine-tune part or all of

the pre-trained model to reach more desired results.

Actually, it is quite hard for us to understand the

correlation between these tasks. In some other

situations if target tasks possess much different

distribution compared with the pre-training datasets,

effectiveness of initialization and fine-tuning may be

largely restricted. This issue is exactly arising in

pathology image classification domain. On one

hand, in light of common human’s perception,

pathology images usually have more complicated

appearances than natural images because it is

difficult to figure out the intuitionistic difference

between benign and malignant images at a glance

due to their color uniformity of H&E (Hematoxylin

and eosin) stain and componential similarity of

tissues. On the other hand, owing to professional

knowledge, pathologists are able to distinguish

various pathological components and structures

within the image. Based on this knowledge, they can

easily tell where abnormality has occurred.

Nevertheless, natural image datasets for pre-training

rarely contain relevant information. From this

perspective, we believe that it is crucial to build a

bridge which can reasonably transfer the neural

networks from the task of pre-training classification

to the final benign/malignant judgment of the well-

annotated pathological images.

Enhanced Deep Learning for Pathology Image Classiﬁcation: A Knowledge Transfer based Stepwise Fine-tuning Scheme

Figure 1: The proposed knowledge transferring based stepwise fine-tuning scheme. Apart from “low-level” pre-training

datasets and “high-level” well-annotated datasets, “medium-level” data are generated automatically and involved in the 1st

fine-tuning process. In CNN models corresponding to training steps along the knowledge transferring direction, darker

nodes in CNN models denote more specialized (deeper) representation which is expected for the pathology image

classification task.

3.1 Making CNNs Learn Pathology

Step by Step

In this paper, we propose a conception taking

advantage of stepwise fine-tuning to make deep

neural networks learn to understand pathology

images gradually following pathologist’s cognitive

way. Before learn to understand the differences

between benign or malignant pathology images, one

should first understand the fundamental pathological

knowledges beforehand. Such knowledges may

include but not be limited to distribution status and

density of cells, degree of nucleus distortion, nucleus

size and nuclear-cytoplasmic ratio. In the previous

section, it has been declared that specific measures

of these indexes for benign/malignant judgement

may be not reliable due to various changing factors.

Nevertheless, these morphological characteristics

can still be exploited to provide rough but task-

relative initialization to the deep neural networks

like training an unskilled pathologist.

To make deep neural networks able to pathology

in a rational way, we build up a stepwise scheme

(Suzuki et al., 2017) in which fine-tuning is adopted

to transfer several different levels of knowledge

toward the final task step by step. The scheme

consists of three main steps: pre-training, 1st fine-

tuning and 2nd fine-tuning. As shown in Figure 1, at

the beginning of the training progress, we have a

pre-trained network as initialization. The following

step of 1st fine-tuning involves a type of target-

correlative “medium-level” dataset, which is

regarded as the carrier of the fundamental

pathological knowledges. According to our

conception, rather than directly driving the deep

neural networks to learn about benign and

malignant, making it gain fundamental pathological

knowledge from the “medium-level” datasets

probably contribute to the task of higher difficulty

(Qu et al., 2018). Therefore, 1st fine-tuning with the

“medium-level” datasets is placed in the middle of

the stepwise scheme. By this step, deep neural

networks are considered more pathology-

specialized. Finally, well-annotated

benign/malignant images are used for the second

time fine-tuning. In the lower part of the figure,

corresponding to all training steps along the

knowledge transferring direction, darker nodes in

CNN models denote more specialized (deeper) rep-

resentation which is expected for the pathology

image classification task When the number of output

BIOIMAGING 2019 - 6th International Conference on Bioimaging

Figure 2: Procedure of generating “medium-level” dataset with color index local auto-correlation (CILAC).

classes changes, the network architecture needs to be

adjusted accordingly. As to the earlier layers, we

recommend to set all of them learnable in both of the

two fine-tuning steps in order to achieve practical

improvement.

3.2 Build “Medium-level” Dataset

using Color Index Local

Auto-Correlation

In the light of out aforesaid conception that

fundamental pathology knowledges are expected to

be involved to bridge the pre-trained model and

transfer these knowledge to the final-task-targeting

model, we consider to adopt a reliable way to

provide with rough and robust, but weakly

pathology-related information to fulfil this demand.

Meanwhile, in respect of the mission of our work, it

is a prerequisite requirement that the “medium-level”

dataset must be achievable at much lower cost than

the well-annotated datasets made by pathologists. In

order to satisfy this requirement, a full-automatic

dataset generation approach is preferentially needed.

According to our earlier study (Qu et al., 2014),

color index local auto-correlation (CILAC,

Kobayashi and Otsu, 2009) has been evidenced as an

independently competent hand-crafted feature in

pathology image classification. Notice that feature

extraction with CILAC is right the choice to

summarily evaluate the status and relation of

background, nuclei and cytoplasm based on the

three-level color indexed image. These three

components are deemed to contain most of the

crucial information for morphological analysis.

Meanwhile, because the color indexing process is

equivalent to normalizing the color space in an

extremely rough way, the color indexed images are

regarded more robust to uneven staining intensity. In

this paper, we take advantage of CILAC based

feature extraction on color-indexed images and

expect to collect sufficient anatomical pathology

information with less noise.

Specifically, CILAC feature was developed on

the basis of Higher Local Auto-Correlation. As

shown in Figure 2, CILAC consists of a set of local

patterns which are able to calculate both the local

auto-correlations of different color levels and their

statistical distribution. CILAC in order N (N = 0, 1,

2) is defined as below:





  







 (1)





    











   (2)





      











  



   (3)

Where 



denotes N-order correlation.  





 



 



  



 is a D-dimensional vector standing

for D color indexes of an color indexed image. 

indicates the reference (central) pixel. a, b are

different displacements of the surrounding inspected

pixels, respectively. 



, 



and 



denote the pixels

taken into account corresponding to all

displacements. In this paper, D is set to 3 according

to three color indexes of the 3-level image. In that

case, the 0th order CILAC (N = 0) draw out different

color indexes themselves, and the 1st and 2nd order

CILAC (N = 1 and N = 2) represent the local co-

Enhanced Deep Learning for Pathology Image Classiﬁcation: A Knowledge Transfer based Stepwise Fine-tuning Scheme

Table 1: Datasets used in experiments.

Data Type

Category

Training

Validation

Test

Medium-level Data

Cluster 1

5,016

558

Cluster 2

3,949

439

High-level

(Well-annotated)

Data

Benign

5,400

1,620

2,700

Malignant

5,400

1,620

2,700

occurrences of different color indexes. Pathological

components including nuclei, cytoplasm and

background are expected synthetically vectorized by

the CILAC patterns.

Practically, we implement a string of automatic

image pre-processing techniques including 3-level

quantization to obtain the background-nuclei-

cytoplasm images. Afterwards, CILAC feature are

extracted from these 3-level images and principal

component analysis (PCA) is also used to reduce the

dimensionality of feature vector space. Next, we

employ unsupervised K-means clustering to separate

images into several clusters within the feature vector

space. Practically, in order to obtain clusters with

large distance as possible, we set the number of

cluster k=3, and select the farthest two clusters in

line with the visualized status within the coordinate

space of finite principal components. Finally, we

pick up the most distant two clusters and assign +1

and -1 to them. Pass through the above series of

operations, the two clusters are available to be

automatically generated and employed as “medium-

level” training data for the 1st step fine-tuning.

4 EXPERIMENTS

4.1 Experimental Procedures

In order to evaluate the effectiveness of our proposed

transfer learning scheme using stepwise fine-tuning

and the automatically produced low-cost “medium-

level” datasets based on CILAC, we make use of

several well-known deep neural networks including

VGG-16 (Simonyan and Zisserman, 2015), AlexNet

and GoogLeNet (hereafter InceptionV3, Szegedy et

al., 2016). With each of the deep neural networks,

we conduct two separate procedures: (1) adopting

fine-tuning only once with high-level well-annotated

pathology images directly upon the model which has

been pre-trained by low-level large-scale datasets

(ImageNet). (2) adopting the 1st fine-tuning and 2nd

fine-tuning in sequence with the “medium-level”

data and high-level well-annotated pathology image

data, respectively. Competitions are carried out

between the two procedures based on the three deep

neural networks stated above.

4.2 Datasets

This paper employs three types of data including

“low-level”, “medium-level” and “high-level” data,

respectively used for the initialization (pre-training),

the 1st stage fine-tuning and the 2nd stage fine-

tuning. In practice, ImageNet data containing

approximately 1.2 million images in 1,000 separate

categories are customary utilized to initialize the

CNN models. As to the “medium-level” data and

high-level well-annotated pathology image data, we

make use of the gastric pathology datasets collected

by two experienced pathologists. All of the data are

illustrated in Table 1. By adopting unsupervised

clustering upon more than 10,000 patches

(256×256), we succeeded to obtain cluster 1

including 5,574 patches and 4,388 patches belong to

cluster 2. In the 1st-step fine-tuning, 90% of patches

in each cluster are used for training, remaining 10%

are used for validation. Validation data are

completely separated from training data so that well-

generalized model can be selected accordingly. As to

the well-annotated “high-level” datasets, in order to

evaluate the efficacy of the proposed two-stage

scheme, we have prepared well-annotated datasets

including 5,400 benign and 5,400 malignant patches.

All of these patches are cut off from whole

pathology images without augmentation. Except

from the former datasets, we additionally use a

validation dataset including 1,620 benign and 1,620

malignant patches to select the best model

configuration, and a test dataset of 2,700 benign and

2,700 malignant patches to finally evaluate the

performance in each optional case. It is noteworthy

BIOIMAGING 2019 - 6th International Conference on Bioimaging

Table 2: Performances of the proposed two-stage fine-tuning using “Medium-level” data.

Scheme

CNN Architecture

VGG-16

AlexNet

GoogLeNet

(Inception V3)

AUC

ACC

Preci-

sion

Recall

AUC

ACC

Preci-

sion

Recall

AUC

ACC

Preci-

sion

Recall

One-step

0.936

0.836

0.96

0.70

0.867

0.794

0.80

0.79

0.881

0.779

0.79

0.78

Two-step

(Proposed)

0.957

0.873

0.87

0.923

0.845

0.85

0.84

0.939

0.865

0.87

0.86

Figure 3: Performances of the proposed two-stage fine-tuning presented by ROC.

that there is no overlap between the “medium-level”

datasets and the “high-level” datasets, and

meanwhile no overlap among the training, validation

and test datasets.

4.3 Results and Discussion

Next, we will present results and discuss about the

rival performances of the regular one-step fine-

tuning and our proposed stepwise fine-tuning

scheme. To be impersonal, we concurrently take

AUC (Area under the curve, which is calculated on

the class-probability output), ACC (accuracy),

Precision and Recall as the evaluation criteria

(Sokolova and Lapalme, 2009).

As denoted in Table 2, notably, in all of the

couples of competitory schemes, our proposed two-

step fine-tuning using “medium-level” dataset has

yield reasonable improvement. Specifically, AUC

value is raised by 0.021, 0.056 and 0.058, when we

adopt CNN architectures VGG-16, AlexNet and

Inception V3, respectively. Meanwhile, if we focus

on ACC values, we are aware of the fact that the

greatest improvement happens when our proposed

scheme using Inception V3 is adopted. The accuracy

has remarkably increased from 0.779 to 0.865.

Besides, precision and recall, which are commonly

used for medical image classification, are presenting

similar trend to AUC and ACC. As more intuitively

illustrated in Figure 3, three CNN architectures have

produced three separate ROC Figures. The red curve

denotes the two-stage scheme using “medium-level”

dataset, while the green curve denotes the

conventional one-stage scheme. It is clear at a

glance, in each figure, our proposed scheme

possesses overwhelming area all along both the false

positive rate axis and true positive rate axis. These

results have illustrated that our proposed scheme is

capable and rarely dependent on the deep neural

network’s architecture and the amount of well-

annotated data. To sum up, the proposed stepwise

fine-tuning scheme employing “medium-level”

dataset automatically produced based on Color-

Index Local Auto-Correlation (CILAC) has

successfully boosted the performance of the pre-

trained neural networks for gastric pathology image

classification in various situations.

5 CONCLUSION

In this paper, aiming to maximize the classification

capacity of deep neural networks and alleviate the

lack of annotated pathology data, we proposed a

stepwise fine-tuning scheme. By extracting

pathology-correlative information from unannotated

pathology images with handcrafted features, and

Enhanced Deep Learning for Pathology Image Classiﬁcation: A Knowledge Transfer based Stepwise Fine-tuning Scheme

making use of these materials as “medium-level”

data to intermediately fine-tune deep neural

networks, we managed to make the deep neutral

networks acquire pathological knowledge step by

step following the way of pathologist’s perception.

By this mean, the initial task and the final target task

are expected to be bridged in a reasonable way. In

the experiments, our proposed scheme exerted

adequate efficacy for boosting the classification

performance and revealed high applicability for

different CNN architectures. Taking the proposed

scheme as seed, it is promising to promote such kind

of stepwise training scheme to more medical image

recognition tasks.

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Enhanced Deep Learning for Pathology Image Classiﬁcation: A Knowledge Transfer based Stepwise Fine-tuning Scheme