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            Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, AZ Amsterdam, 1105, Netherlands            
            Amsterdam Movement Sciences, BT Amsterdam, 1081, Netherlands            
            Department of Medical Physics, Isala, 8025 AB Zwolle, Netherlands            
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	&lt;pre class=&quot;comment&quot; style=&quot;margin-top: 0; margin-bottom: 0;&quot;&gt;&lt;u&gt;Megjegyzés&lt;/u&gt;: Department of Radiology, Isala, 8025 AB Zwolle, Netherlands            
            Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, AZ Amsterdam, 1105, Netherlands            
            Amsterdam Movement Sciences, BT Amsterdam, 1081, Netherlands            
            Department of Medical Physics, Isala, 8025 AB Zwolle, Netherlands            
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            Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, 610041, China            
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            Correspondence Address: Pan, X.; Department of Radiology, China; email: hx_pxl@163.com</comment>
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&lt;div class=&quot;lastModified&quot;&gt;Utolsó módosítás: 2024.12.04. 14:48 Csajbók Edit (SE_KK_Admin5_CSE, admin)
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	&lt;pre class=&quot;comment&quot; style=&quot;margin-top: 0; margin-bottom: 0;&quot;&gt;&lt;u&gt;Megjegyzés&lt;/u&gt;: West China School of Clinical Medicine, Sichuan University, Chengdu, 610041, China            
            Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China            
            Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, 610041, China            
            Export Date: 16 November 2024            
            Corresponde...&lt;/pre&gt;

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            Clinical Hospital Center Rijeka, Krešimirova 42, Rijeka, 51000, Croatia            
            Faculty of Medicine, University of Rijeka, Branchetta 20/1, Rijeka, 51000, Croatia            
            Cited By :7            
            Export Date: 16 November 2024            
            Correspondence Address: Mrzljak, V.; Faculty of Engineering, Vukovarska 58, Croatia; email: vmrzljak@riteh.hr</comment>
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      <abstractText>Simple Summary: Bladder cancer is a common cancer of the urinary tract, characterized by high metastatic potential and recurrence. The research applies a transfer learning approach on CT images (frontal, axial, and saggital axes) for the purpose of semantic segmentation of areas affected by bladder cancer. A system consisting of AlexNet network for plane recognition, using transfer learning-based U-net networks for the segmentation task. Achieved results show that the proposed system has a high performance, suggesting possible use in clinical practice.Abstract: Urinary bladder cancer is one of the most common cancers of the urinary tract. This cancer is characterized by its high metastatic potential and recurrence rate. Due to the high metastatic potential and recurrence rate, correct and timely diagnosis is crucial for successful treatment and care. With the aim of increasing diagnosis accuracy, artificial intelligence algorithms are introduced to clinical decision making and diagnostics. One of the standard procedures for bladder cancer diagnosis is computer tomography (CT) scanning. In this research, a transfer learning approach to the semantic segmentation of urinary bladder cancer masses from CT images is presented. The initial data set is divided into three sub-sets according to image planes: frontal (4413 images), axial (4993 images), and sagittal (996 images). First, AlexNet is utilized for the design of a plane recognition system, and it achieved high classification and generalization performances with an (AUC(micro)) over bar of 0.9999 and sigma(AUC(micro)) of 0.0006. Furthermore, by applying the transfer learning approach, significant improvements in both semantic segmentation and generalization performances were achieved. For the case of the frontal plane, the highest performances were achieved if pre-trained ResNet101 architecture was used as a backbone for U-net with (DSC) over bar up to 0.9587 and sigma(DSC) of 0.0059. When U-net was used for the semantic segmentation of urinary bladder cancer masses from images in the axial plane, the best results were achieved if pre-trained ResNet50 was used as a backbone, with a DSC up to 0.9372 and sigma(DSC) of 0.0147. Finally, in the case of images in the sagittal plane, the highest results were achieved with VGG-16 as a backbone. In this case, (DSC) over bar values up to 0.9660 with a sigma(DSC) of 0.0486 were achieved. From the listed results, the proposed semantic segmentation system worked with high performance both from the semantic segmentation and generalization standpoints. The presented results indicate that there is the possibility for the utilization of the semantic segmentation system in clinical practice.</abstractText>
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      <label>Segota Sandi Baressi et al. Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach. (2021) BIOLOGY-BASEL 2079-7737 10 11</label><template>&lt;div class=&quot;JournalArticle Publication short-list&quot;&gt; &lt;div class=&quot;authors&quot;&gt; &lt;span class=&quot;author-name&quot; &gt; Segota, Sandi Baressi &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Lorencin, Ivan &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Smolic, Klara &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Andelic, Nikola &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Markic, Dean &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Mrzljak, Vedran ✉ &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Stifanic, Daniel &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Musulin, Jelena &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Spanjol, Josip &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; ; &lt;span class=&quot;author-name&quot; &gt; Car, Zlatan &lt;/span&gt; &lt;span class=&quot;author-type&quot;&gt; &lt;/span&gt; &lt;/div &gt;&lt;div class=&quot;title&quot;&gt;&lt;a href=&quot;/gui2/?mode=browse&amp;params=publication;33436891&quot; mtid=&quot;33436891&quot; target=&quot;_blank&quot;&gt;Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach&lt;/a&gt;&lt;/div&gt; &lt;div class=&quot;pub-info&quot;&gt; &lt;span class=&quot;journal-title&quot;&gt;BIOLOGY-BASEL&lt;/span&gt; &lt;span class=&quot;journal-volume&quot;&gt;10&lt;/span&gt; : &lt;span class=&quot;journal-issue&quot;&gt;11&lt;/span&gt; &lt;span class=&quot;page&quot;&gt; Paper: 1134 , 25 p. &lt;/span&gt; &lt;span class=&quot;year&quot;&gt;(2021)&lt;/span&gt; &lt;/div&gt; &lt;div class=&quot;pub-end&quot;&gt;&lt;div class=&quot;identifier-list&quot;&gt; &lt;span class=&quot;identifiers&quot;&gt; &lt;span class=&quot;id identifier oa_none&quot; title=&quot;none&quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;10.3390/biology10111134&quot; target=&quot;_blank&quot; href=&quot;https://doi.org/10.3390/biology10111134&quot;&gt; DOI &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;id identifier oa_none&quot; title=&quot;none&quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;000726575400001&quot; target=&quot;_blank&quot; href=&quot;https://www.webofscience.com/wos/woscc/full-record/000726575400001&quot;&gt; WoS &lt;/a&gt; &lt;/span&gt; &lt;span class=&quot;id identifier oa_none&quot; title=&quot;none&quot;&gt; &lt;a style=&quot;color:blue&quot; title=&quot;85119016618&quot; target=&quot;_blank&quot; href=&quot;http://www.scopus.com/record/display.url?origin=inward&amp;eid=2-s2.0-85119016618&quot;&gt; Scopus &lt;/a&gt; &lt;/span&gt; &lt;/span&gt; &lt;/div&gt; &lt;div class=&quot;short-pub-prop-list&quot;&gt; &lt;span class=&quot;short-pub-mtid&quot;&gt; Közlemény:33436891 &lt;/span&gt; &lt;span class=&quot;status-holder&quot;&gt;&lt;span class=&quot;status-data status-VALIDATED&quot;&gt; Egyeztetett &lt;/span&gt;&lt;/span&gt; &lt;span class=&quot;pub-core&quot;&gt; Idéző &lt;/span&gt; &lt;span class=&quot;pub-type&quot;&gt;Folyóiratcikk (Szakcikk ) &lt;/span&gt; &lt;!-- &amp;&amp; !record.category.scientific --&gt; &lt;span class=&quot;pub-category&quot;&gt;Tudományos&lt;/span&gt; &lt;/div&gt; &lt;/div&gt; &lt;/div&gt;</template><template2>&lt;div class=&quot;JournalArticle Publication long-list&quot;&gt;
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		&lt;div class=&quot;autype autype0&quot;&gt;				&lt;span class=&quot;author-name&quot; &gt;Segota Sandi Baressi
    &lt;/span&gt;
;&amp;nbsp;&amp;nbsp;&amp;nbsp;
							&lt;span class=&quot;author-name&quot; &gt;Lorencin Ivan
    &lt;/span&gt;
;&amp;nbsp;&amp;nbsp;&amp;nbsp;
							&lt;span class=&quot;author-name&quot; &gt;Smolic Klara
    &lt;/span&gt;
;&amp;nbsp;&amp;nbsp;&amp;nbsp;
							&lt;span class=&quot;author-name&quot; &gt;Andelic Nikola
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;&amp;nbsp;&amp;nbsp;&amp;nbsp;
							&lt;span class=&quot;author-name&quot; &gt;Markic Dean
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;&amp;nbsp;&amp;nbsp;&amp;nbsp;
							&lt;span class=&quot;author-name&quot; &gt;Mrzljak Vedran ✉
    &lt;/span&gt;
;&amp;nbsp;&amp;nbsp;&amp;nbsp;
							&lt;span class=&quot;author-name&quot; &gt;Stifanic Daniel
    &lt;/span&gt;
;&amp;nbsp;&amp;nbsp;&amp;nbsp;
							&lt;span class=&quot;author-name&quot; &gt;Musulin Jelena
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;&amp;nbsp;&amp;nbsp;&amp;nbsp;
							&lt;span class=&quot;author-name&quot; &gt;Spanjol Josip
    &lt;/span&gt;
;&amp;nbsp;&amp;nbsp;&amp;nbsp;
							&lt;span class=&quot;author-name&quot; &gt;Car Zlatan
    &lt;/span&gt;

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&lt;/div&gt;
&lt;div class=&quot;title&quot;&gt;&lt;a href=&quot;/gui2/?mode=browse&amp;params=publication;33436891&quot; target=&quot;_blank&quot;&gt;Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach&lt;/a&gt;&lt;/div&gt;    &lt;div&gt;		&lt;span class=&quot;journal-title&quot;&gt;BIOLOGY-BASEL&lt;/span&gt;

        &lt;span class=&quot;journal-issn&quot;&gt;( &lt;a target=&quot;_blank&quot; href=&quot;https://portal.issn.org/resource/ISSN/2079-7737&quot;&gt;2079-7737&lt;/a&gt;)&lt;/span&gt;:
		&lt;span class=&quot;journal-volume&quot;&gt;10&lt;/span&gt; &lt;span class=&quot;journal-issue&quot;&gt;11&lt;/span&gt;
&lt;span class=&quot;page&quot;&gt;
		Paper 1134.
	 25 p. 
&lt;/span&gt;		 &lt;span class=&quot;year&quot;&gt;(2021)&lt;/span&gt;  
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&lt;div class=&quot;lastModified&quot;&gt;Utolsó módosítás: 2022.12.22. 23:55 Szuper Admin (admin)
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	&lt;pre class=&quot;comment&quot; style=&quot;margin-top: 0; margin-bottom: 0;&quot;&gt;&lt;u&gt;Megjegyzés&lt;/u&gt;: Faculty of Engineering, University of Rijeka, Vukovarska 58, Rijeka, 51000, Croatia            
            Clinical Hospital Center Rijeka, Krešimirova 42, Rijeka, 51000, Croatia            
            Faculty of Medicine, University of Rijeka, Branchetta 20/1, Rijeka, 51000, Croatia            
            Cited By :7            
            Export Date: 16 November 2024            
        ...&lt;/pre&gt;

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