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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5533_Библиотеки_им_академика_М_И_Перельмана.pdf
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
efficiently analyze the threat
of EGC and help doctors in
strengthening the diagnosis
approach than DT and LR
and assessment of EGC
213 Sn, 71%; Sp96.1%
Table 10.5. The use of AI to forecast prognosis.
Authors Year Disease Algorithm Prognosis forecast No. of cases Results
Chien et al [18] 2008 GC ANN, DT, LR An after-surgery problem 521 ANN was a more effective
tumor removal operation
Jagric et al [136] 2010 GC LVQNN Liver metastases following a
Lai et al [138] 2008 Primary GC ANN Tumor stage predicted before surgery 121 Accuracy,81.82%
Liu et al [134] 2018 EGC data analysis Invasive-free screening 618 Accuracy, 77.84%; AI can
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EGC is for early gastric cancer; DT stands for decision tree; LR stands for logistic regression; and LVQNN stands for learning vector quantization neural networks,
ANN stands for artificial neural network.
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
in the proposed five-year survival prediction model. The proposed approach beat the
Cox regression model with a precision of 83.5%. A ResNet-based model was put
forth to forecast the individuals with AGC’s highest overall survival rate [133].
Additionally, based on preoperative CT images, A DL-based imaging signature
(DeLIS) was created by Jiang et al [131] to predict survival without disease and
prolonged survival for GC patients. In a multicenter investigation, the suggested
DeLIS demonstrated independent predictive value from conventional clinicopathologic variables. With a C-index of 0.792–0.802 and a net re-categorization
increase of 10.1%–28.3%, the integrated model improved performance by combining
imaging signals and clinical criteria. They also found that adjuvant therapy would
be more beneficial for those with higher DeLIS scores. Additionally, researchers
looked at AI-based digital pathology prognosis.
Pathologists often assess positive cells for prognosis by counting them in certain
perspectives. Subjectivity and inter-observer heterogeneity, however, compromised
its accuracy. Based on tissue microarrays stained with immuno-histochemistry, a
DL model without hypotheses was created to predict risk. The revised Google Net
was subjected to the application of loss functions (Cox loss, Uno loss, and Log-rank
loss) to account for the time-to-event element of survival information. A group of
proliferative markers (Ki67) and immune cell markers (CD8, CD20, and CD68)
were also included in their investigation of the tumor microenvironment.
Bollschweiler et al found that the immune-related CNN score generated can add
to prognosis prediction utilizing qualitative analysis [135].
Additionally, N staging is an important prognostic marker, and pathologists must
laboriously examine and count metastatic lymph nodes (MLNs) in order to
accurately determine it. As a DL framework to examine the lymph-node WSI, the
overall tumor-area-to-MLN-area ratio (T/MLN), which was discovered to be a selfsufficient prognostic predictor, was presented. The system employs U-net for
segmentation, ResNet for categorization, and T/MLN for computation. Using
just N phases, tests using multicenter data sets show that T/MLN is an effective
predictive measure with a hazard ratio of 2.05 and a greater C-index of 0.694 above
0.646 [146].
10.9 Conclusion and future prospects
In 1955, John McCarthy was t he first to use the phrase AI. The ability of machines
to learn and solve problems is the basis for the notion of AI. AI is divided into five
primary subfields: ANNs, DL, vision in computers, natural language processing
(NLP), and ML. Figure 10.3 illustrates the relationship between ML, AI, and DL.
While DL is built on the intricate ANN designs, ML creates algorithms to analyse
data. NLP is a field that gives computers the ability to extract meaningful data
from content, like digital health records, allowing in-depth research (figure 10.3).
ANNs simulate the way the nervous system functions in humans, which has
multiple layers and can recognize subtle or complicated patterns. For saved photos
or live videos, computer vision does machine recognition. There are numerous uses
for computer vision, such as image-guided surgery, digital pathology
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Figure 10.3. AI concept.
Figure 10.4. AI applications in the treatment of cancer of the stomach.
interpretation, and computer-assisted endoscopic diagnostics. Recent investigations have shown that it has great potential for use in the treatment of stomach
cancer. AI can help with early detection, treatment planning, and prognosis
forecasting in cases of stomach cancer. The primary uses of AI in stomach cancer
are illustrated in (figures 10.4 and 10.5).
Future clinical practice in the era of precision medicine will require the integration of
omics data with clinical information. The workload of doctors could be significantly
reduced with the help of AI technology. In recent years, AI-assisted endoscopic
diagnosis for stomach cancer has significantly improved thanks to picture recognition.
These researches mainly concentrate on four areas: identifying HP infection, identifying CAG, detecting EGC and estimating the degree of invasion. High detection rate
and processing speed were shown by an AI-assisted system. Additionally, when used
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
Figure 10.5. AI implementation in GC.
for video images, real-time identification together with endoscopic inspection were
evaluated, although further study is required before it can be implemented in clinical
practice in the future. Additionally, it is utilized to develop models that predict surgical
complications and lymph node metastasis, therapeutic prognosis and the reaction. In
addition, a different study found that an ANN model outperformed a decision tree
model at predicting hospital charges [135].
The use of AI approaches does have some restrictions, though. First, there aren’t
many high-quality instruction and approval datasets available. Second, most AI
algorithms are ‘black boxes,’ meaning that no one has a complete grasp of how they
operate. The use of AI upon patient permission and identification of the misdiagnosis’s perpetrator or inappropriate course of therapy are two additional ethical and
safety concerns. AI is also unable to establish causal connections. Physicians must
rigorously assess and interpret AI-generated predictions in ways that have therapeutic relevance [125]. Additionally, most modern systems use supervised learning,
which is based on carefully human-labeled data. However, given the dearth of
domain experts, it is challenging to collect a sizable volume of high-quality labeled
data for medical activities, while more unlabeled data remain untapped. To make
use of the underutilized unlabeled data, numerous strategies for self- and semisupervised learning have lately been introduced. These methods might be applied in
GC AI to address the problem of sparsely tagged data [130].
In the early identification, diagnosis, and treatment of disease, many diagnostic
imaging techniques can play special and unique roles in decision-making processes
related to GC. While pathologic investigation must be used to confirm GC, its
preliminary diagnosis is acceptable for endoscopic investigation. Additionally, AGC
may be identified using CT scanning, hence it cannot be utilized to make the
diagnosis. The use of a single modality to inform decisions could result in incorrect
diagnosis. Building multi-modality algorithms is advantageous in order to reach
more thorough diagnostic conclusions. Additionally, large-scale imaging data can
be gathered more affordably than expensive molecular data. In order to understand
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
the connection between disease-related gene expression patterns and imaging
characteristics, a new research field called radio genomics has arisen [114].
For glioblastoma [109], carcinoma of the breast [110], and malignancies of the
lungs [111], it has drawn a lot of attention and it may be able to help with better
treatment selection, prognosis forecasting, and cancer knowledge in general.
Researchers working across cancers may advance our knowledge of tumor-related
chemicals given that pan-cancer compounds are present in a variety of tumor types.
The Database of Pan-Cancer, developed by TGGA, for forms of human carcinoma
is reclassified based on their molecular similarity [112]. Knowledge from cancers
with similar characteristics may be applied to the study of GC. Additionally, the
majority of previous research has concentrated on a small number of modeling
issues, such as diagnosing precancerous conditions or tumors, categorizing the
phases or cancer subgroups, and predicting treatment effectiveness or survival
outcomes. These duties are, nevertheless, inevitably related. In order to create a
more reliable GC diagnosis, multi-task learning algorithms may be able to take
advantage of useful information by learning several related tasks at once [113]. The
tumor’s growth is also an unpredictable process that takes place over time.
The evolution of pictures over time may be crucial in determining the prognosis
and responsiveness to treatment. Building time-dependent modeling based on image
information from different patient phases, such as initial detection, pre-therapy, post
therapy, etc, may therefore be beneficial. It might aid in our understanding of the
dynamic process through which cancer develops. Additionally, given that different
modalities are used at various stages of GC, combining multi-modality and multitask algorithms may enhance prediction accuracy.
Epigenetic, genomes, proteomics, metabolomics, and other fields fall under the
purview of using AI to find novel targets for cancer therapy. Because single omics
studies cannot accurately predict anticancer targets, we must use ML and computational biology investigation to effectively incorporate multiple omics data, address
the complexity of cancer caused by interactions between genes and their products,
and advance our understanding of carcinogenesis. How to integrate multiomics data
and uncover new anticancer targets using AI biological analytical techniques will
thus be a key focus of future research.
There is still much room for development before this technology becomes
genuinely widespread. It takes time to adjust and develop methods that are not
just trustworthy but also understandable and accessible for incorporation of this
magnitude. The major problem with AI as well as DL is that they effectively rely on
the vast volumes of data that are provided to them in order to learn and grow [67].
Issues arise because datasets are not readily available, which may limit their
expansion [67]. The necessity for drawing relevant inferences from the massive
amounts of information that are currently available may be addressed with the use
of tools like data mining, but these techniques have limits since additional datasets
result in more accurate and significant data [68].
Despite these difficulties, the adoption of AI in the treatment of cancer looks
promising in the age of personalized medicine. It may be considered as an aid for
endoscopic diagnostics to catch sickness early and determine how deep the invasion
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
has gone. Digital pathology can also be used to categorize the many types of cancer
and determine how each one responds to various pharmacological treatments. AI’s
application in detecting stomach cancer is expanding rapidly due to the quick the
advancement of ML and digital pathology techniques. AI has been used in a variety
of settings, such as endoscopic diagnosis, assessing the degree of membrane
invasion, histopathologically diagnosis, and classification, analyzing gastric smears,
molecular and genetic screening, and prognosis prediction.
Applications of AI technology in the diagnosis of gastrointestinal cancer have
demonstrated rapidity and precision comparable to or even superior to that of
conventional pathologists. However, pathologists’ breadth and contextual knowledge will not be replaced by AI; rather, accuracy and effectiveness of pathologists in
diagnosing stomach cancer can only be increased by their combination. The
generalization of AI models [56], black boxes [54–56], as well as a scarcity of
datasets of excellent quality are only a few of the issues that researchers have
discovered with AI technology. Future work in this area of AI will probably
concentrate on finding solutions to these issues. First, there is a lack of consensus
regarding the inner workings of AI systems. One difficulty is deciphering and
comprehending the decision-making processes of sophisticated AI models. One of
the primary obstacles to the clinical use of algorithms that use DL is a situation
known as the ‘black box problem, ’ which is one of the key obstacles, and is the
difficulty of reading and comprehending how sophisticated AI models arrive at
judgments. The accuracy of DL may be impacted by flaws including spectral bias
(class imbalance), over fitting, and selection bias. Graph neural networks (GNN)
offer an edge over DL AI techniques like CNN models because they have
demonstrated a lack of interpretability. GNNs are likely to be used increasingly
in the diagnosis of stomach cancer in the future. The GNN’s comprehensibility will
make it possible as medical AI models acquire novel understandings beyond what is
currently known to humans, researchers are able to use these new insights to better
understand the biochemical mechanisms underlying disease.
Additionally, adopting AI frequently involves data problems. For instance, there
aren’t many high-quality datasets accessible for validation and training. To address
the issue of insufficient data, we might take the Federated Learning (FL) method
into consideration [57]. With the assumption of restricted data sharing, by transferring system parameters from the main database, FL is a multi-distributed joint
learning approach that can learn across several databases and create a highprecision system. We show that FL allows us to efficiently build without the hassle
of direct information exchange; weakly guided DL models are created from
scattered data repositories and with differential privacy maintained via random
noise creation.
The application of AI models and technologies for supporting medical decisions
in general is the final crucial question. When presented with data from different
sources, AI models using a single source of data could not work properly.
Additionally, the ability to train AI models and validate them using datasets from
many sources will increase generalization. The thorough examination of AI can also
include multidisciplinary data from fields like gastroenterology, radiation therapy,
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Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
pathological conditions, medical care, surgery, and radiation oncology for difficult
situations. On the other hand, researchers may not always have access to the
substantial computational power and massive data support that AI technology is
relying on. Given these conditions, it is foreseeable that the specialization of AI
technology for stomach cancer (a single illness or even a single subgroup) may be a
practical solution. The entire surgical procedure will be supported by AI-assisted
technologies. A harmonious coexistence of humans and technology is the best
situation for achieving peak performance, thus AI will not entirely replace doctors.
The study of stomach cancer has advanced significantly thanks to improvements
in AI approaches, particularly ML and DL. This chapter offers a thorough overview
of the current state and potential developments in AI-assisted diagnosis and
prognosis. In comparison to traditional statistical methods, many researchers have
observed that AI produces outstanding outcomes. The diagnosis and prognosis of
stomach cancer will be revolutionized by AI’s effective computational power and
learning capabilities, despite the technology’s drawbacks and difficulties, for
instance, the need for interpretable models and well-annotated data.
EGC detection is challenging and necessitates thorough endoscopic evaluation.
According to studies, roughly 10% of instances of stomach cancer had previously
negative endoscopies. This number can be lowered with better endoscopic assessment. Real-time systems that are capable of identifying and characterizing premalignant/malignant gastric lesions are being created thanks to the availability of more
modern endoscopic imaging techniques and AI. Cross-sectional imaging techniques
might not be able to detect these small malignant gastric lesions, hence adequate
endoscopic evaluation of EGC is essential for choosing the best resection procedure.
ESD provides a surgical resection substitute for a subset of EGC patients. It is also
essential to plan endoscopic monitoring of high-risk individuals, premalignant
lesions, and after EGC resection for the early detection and successful therapy of
gastric cancer.
To summarize, current studies of AI in relation to GC primarily focus on
diagnosis. While AI technology is impressive, it may not be widely applicable in
clinical settings just yet. It is possible that few AI researchers have a background in
clinical practice. However, the development of less invasive laparoscopic surgical
techniques might be used for AI and other computer-assisted procedures. The
majority of clinical problems may be replaced with AI-based queries in the near
future. While global medical resource imbalances may hinder the usage of AI
widely, locations with sufficient AI and medical resources may accelerate its
development. Clinicians with AI backgrounds may be critical players in advancing
AI technology. By utilizing AI-based tools to standardize clinical administration,
they can lessen the detrimental effects of accidents in their working life.
References
[1] Ajani J A, Bentrem D J, Besh S et al 2013 National comprehensive cancer network. Gastric
cancer, version 2.2013 J. Natl. Compr. Canc. Netw.
10-24
11 531–46
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
[2] Isobe Y, Nashimoto A, Akazawa K et al 2011 Gastric cancer treatment in Japan: 2008
annual report of the JGCA nationwide registry Gastric Cancer
[3] Veitch A M, Uedo N, Yao K et al 2015 Optimizing early upper gastrointestinal cancer
detection at endoscopy Nat. Rev. Gastroenterol. Hepatol.
[4] Pimentel-Nunes P, Dinis-Ribeiro M, Ponchon T et al 2015 Endoscopic submucosal
dissection: European society of gastrointestinal endoscopy (ESGE) guideline Endoscopy
47 829–54
[5] Wagner A D, Syn N L, Moehler M et al 2017 Chemotherapy for advanced gastric cancer
Cochrane Database Syst. Rev.
[6] Gravalos C and Jimeno A 2008 HER2 in gastric cancer: a new prognostic factor and a
novel therapeutic target Ann. Oncol.
[7] Sauerbruch T, Schreiber M, Schussler P et al 1984 Endoscopy in the diagnosis of gastritis
Endoscopy
[8] Watanabe K, Nagata N, Shimbo T et al 2013 Accuracy of endoscopic diagnosis of
Helicobacter pylori infection according to level of endoscopic experience and the effect of
training BMC Gastroenterol.
[9] Menon S and Trudgill N 2014 How commonly is upper gastrointestinal cancer missed at
endoscopy? A meta-analysis Endosc. Int. Open
[10] Gillies R J, Kinahan P E and Hricak H 2016 Radiomics: images are more than pictures,
they are data Radiology
[11] Andreu-Perez J, Poon C C, Merrifield R D, Wong S T and Yang G Z 2015 Big data for
health IEEE J. Biomed. Health Inform.
[12] Colom R, Karama S, Jung R E and Haier R J 2010 Human intelligence and brain networks
Dialogues Clin. Neurosci.
[13] Li L et al 2020 Convolutional neural network for the diagnosis of early gastric cancer based
on magnifying narrow band imaging Gastric Cancer
[14] Hashimoto D A, Rosman G, Rus D and Meireles O R 2018 Artificial intelligence in
surgery: promises and perils Ann. Surg.
[15] Fard M J, Ameri S, Darin Ellis R, Chinnam R B, Pandya A K and Klein M D 2018
Automated robot-assisted surgical skill evaluation: predictive analytics approach Int. J.
Med. Robot.
[16] Biglarian A, Hajizadeh E, Kazemnejad A and Zayeri F 2010 Determining of prognostic factors
in gastric cancer patients using artificial neural networks Asian Pac. J. Cancer Prev. 11 533–6
[17] Lee J et al 2018 deep learning-based survival analysis identified associations between
molecular subtype and optimal adjuvant treatment of patients with gastric cancer JCO
Clin. Cancer Inform.
[18] Chien C W, Lee Y C, Ma T et al 2008 The application of artificial neural networks and
decision tree model in predicting post-operative complication for gastric cancer patients
Hepato-Gastroenterology 55 1140–5
[19] Turing A M 1950 Computing machinery and intelligence Mind
[20] Russell S and Norvig P 2009 Artificial intelligence: a modern approach, Appl. Mech. Mater.
263 2829–33
[21] Rajpurkar P, Chen E, Banerjee O and Topol E J 2022 AI in health and medicine Nat. Med.
28 31–8
[22] Jordan M I and Mitchell T M 2015 Machine learning: trends, perspectives, and prospects
Science
16 101–4
14 e1850
2 1–14
349 255–60
8 CD004064
19 1523–9
13 128
2 E46–50
278 563–77
19 1193–208
12 489–501
23 126–32
268 70–6
14 301–16
12 660–7
59 433–60
10-25
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
[23] Topol E J 2019 High-performance medicine: the convergence of human and artificial
intelligence Nat. Med.
[24] Lecun Y, Bengio Y and Hinton G 2015 Deep learning Nature 521 436–44
[25] Deo R C 2015 Machine learning in medicine Circulation 132 1920–30
[26] Yeung S, Downing N L, Fei-Fei L et al 2018 Bedside computer vision—moving arti ficial
intelligence from driver assistance to patient safety N. Engl. J. Med.
[27] Gulshan V, Peng L, Coram M et al 2016 Development and validation of a deep learning
algorithm for detection of diabetic retinopathy in retinal fundus photographs JAMA
2402–10
[28] Esteva A, Kuprel B, Novoa R A et al 2017 Dermatologist-level classification of skincancer
with deep neural networks Nature
[29] Ehteshami Bejnordi B, Veta M, Johannes van Diest P et al 2017 Diagnostic assessment of
deep learning algorithms for detection of lymph node metastases in women with breast
cancer JAMA
[30] Yang Y J and Bang C S 2019 Application of artificial intelligence in gastroenterology World
J. Gastroenterol.
[31] Le Berre C, Sandborn W J, Aridhi S et al 2020 Application of artificial intelligence to
gastroenterology and hepatology Gastroenterology
[32] Karakitsos P, Stergiou E B, Pouliakis A et al 1996 Potential of the back propagation neural
network in the discrimination of benign from malignant gastric cells Anal. Quant. Cytol.
Histol. 18 245–50
[33] Karakitsos P, Stergiou E B, Pouliakis A et al 1997 Comparative study of artificialneural
networks in the discrimination between benign from malignant gastric cells Anal. Quant.
Cytol. Histol. 19 145–52
[34] Karakitsos P, Pouliakis A, Koutroumbas K et al 2000 Neural network application inthe
discrimination of benign from malignant gastric cells Anal. Quant. Cytol. Histol. 22 63–9
[35] Amin M B, Greene F L, Edge S B, Compton C C, Gershenwald J E, Brookland R K,
Meyer L, Gress D M, Byrd D R and Winchester D P 2017 The eighth edition AJCC cancer
staging manual: continuing to build a bridge from a population-based to a more
‘personalized’ approach to cancer staging CA Cancer J. Clin.
[36] Sano T, Coit D G, Kim H H, Roviello F, Kassab P, Wittekind C, Yamamoto Y and
Ohashi Y 2017 Proposal of a new stage grouping of gastric cancer for TNM classification:
international gastric cancer association staging project Gastric Cancer
[37] Liu D Y, Gan T, Rao N N, Xing Y W, Zheng J, Li S, Luo C S, Zhou Z J and Wan Y L 2016
Identification of lesion images from gastrointestinal endoscope based on feature extraction of
combinational methods with and without learning process Med. Image Anal.
[38] Ali H, Yasmin M, Sharif M and Rehmani M H 2018 Computer assisted gastric
abnormalities detection using hybrid texture descriptors for chromo endoscopy images
Comput. Methods Programs Biomed.
[39] Luo H et al 2019 Real-time artificial intelligence for detection of upper gastrointestinal cancer
by endoscopy: a multicentre, case-control, diagnostic study Lancet Oncol.
[40] Sakai Y, Takemoto S, Hori K, Nishimura M, Ikematsu H, Yano T and Yokota H 2018
Automatic detection of early gastric cancer in endoscopic images using a transferring
convolutional neural network Conf. Proc. IEEE Eng. Med. Biol. Soc. pp 4138–41
318 2199–210
25 44–56
378 1271–3
316
542 115–8
25 1666–83
158 76–94, e2
67 93–9
20 217–25
32 281–94
157 39–47
20 1645–54
10-26
Nanobiotechnology and Artificial Intelligence in Gastrointestinal Diseases
[41] Bollschweiler E H, Mönig S P, Hensler K, Baldus S E, Maruyama K and Hölscher A H
2004 Artificial neural network for prediction of lymph node metastases in gastric cancer: a
phase II diagnostic study Ann. Surg. Oncol.
[42] Hensler K, Waschulzik T, Mönig S P, Maruyama K, Hölscher A H and Bollschweiler E 2005
Quality-assured efficient engineering of feedforward neural networks (QUEEN) - prether-
apeutic estimation of lymph node status in patients with gastric carcinoma Methods Inf. Med.
44 647–54
[43] Wang H, Ding S, Wu D S, Zhang Y T and Yang S L 2019 Smart connected electronic
gastroscope system for gastric cancer screening using multi-column convolutional neural
networks Int. J. Prod. Res.
[44] Bergholt M S, Zheng W, Lin K, Ho K Y, Teh M, Yeoh K G, Yan So J B and Huang Z
2011 In vivo diagnosis of gastric cancer using Raman endoscopy and ant colony
optimization techniques Int. J. Cancer
[45] Duraipandian S, Sylvest Bergholt M, Zheng W, Yu Ho K, Teh M, Guan Yeoh K, Bok Yan
So J, Shabbir A and Huang Z 2018 Real-time Raman spectroscopy for in vivo, online
gastric cancer diagnosis during clinical Proceedings of the 15th IEEE Int. Symposium on
Biomedical Imaging (Washington, DC: IEEE) pp 182–5
[46] Sharma H, Zerbe N, Klempert I, Hellwich O and Hufnagl P 2017 Deep convolutional
neural networks for automatic classification of gastric carcinoma using whole slide images
in digital histopathology Comput. Med. Imaging Graph.
[47] Leon F, Gelvez M, Jaimes Z, Gelvez T and Arguello H 2019 Supervised classification of
histopathological images using convolutional neuronal networks for gastric cancer detec-
tion STSIVA 2019: Proceedings of the 22nd Symposium on Image, Signal Processing and
Artificial Vision (Bucaramanga, Colombia, 2019 April 24–26) (New York: IEEE)
[48] Iizuka O, Kanavati F, Kato K, Rambeau M, Arihiro K and Tsuneki M 2020 Deep learning
models for histopathological classification of gastric and colonic epithelial tumours Sci.
Rep.
10 1504
[49] Yoshida H, Shimazu T, Kiyuna T, Marugame A, Yamashita Y, Cosatto E, Taniguchi H,
Sekine S and Ochiai A 2018 Automated histological classification of whole-slide images of
gastric biopsy specimens Gastric Cancer
[50] Garcia E, Hermoza R, Castanon C B, Cano L, Castillo M and Castaneda C 2017
Automatic lymphocyte detection on gastric cancer IHC images using deep learning CBMS
2017: Proc. 30th IEEE Int. Symp. on Computer-Based Medical Systems(2017 June 22–24)
(Thessaloniki, Greece) (New York: IEEE) pp 200–4
[51] Liang Q, Nan Y, Coppola G, Zou K, Sun W, Zhang D, Wang Y and Yu G 2019 Weakly
supervised biomedical image segmentation by reiterative learning IEEE J. Biomed. Health
Inform.
[52] Qu J, Hiruta N, Terai K, Nosato H, Murakawa M and Sakanashi H 2018 Gastric
pathology image classification using stepwise fine-tuning for deep neural networks
J. Healthc. Eng.
[53] Huang Z, Liu D, Chen X, Yu P, Wu J, Song B, Hu J and Wu B 2020 Retrospective imaging
studies of gastric cancer: study protocol clinical trial (SPIRIT Compliant) Medicine
(Baltimore)
[54] Gao Y et al 2019 Deep neural network-assisted computed tomography diagnosis of
metastatic lymph nodes from gastric cancer Chin. Med. J. (Engl.)
23 1205–14
2018 8961781
99 e19157
57 6795–806
11 506–11
128 2673–80
61 2–13
21 249–57
132 2804–11
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