Update publications.html

publications.html

@@ -1292,6 +1292,47 @@ <h3>2022</h3>
             -- MICCAI 2022},
 year      = {2022}
 }
+            </pre>
+            <script language="javascript" type="text/javascript" xml:space="preserve">
+              hideblock('ZhengBIBE2022Abs');
+              hideblock('ZhengBIBE2022Bib');
+            </script>
+            </td>
+        </tr> <!-- Paper End Here -->
+
+        <tr> <!-- An Paper -->
+          <td width="22%" valign="top"><p>
+            <img src="images/src/article_li_miccai_2022.png" alt="Result" width="200">
+          </p></td>
+          <td width="78%" valign="top">
+            <p>
+                <b>Histopathology Cross-Modal Retrieval based on Dual-Transformer Network</b> <a href="https://ieeexplore.ieee.org/document/9973633" target="_blank"><i class="fa fa-external-link"></i></a>
+                <br>
+                <font size="3pt" face="Georgia"><i>
+                    Dingyi Hu, <a href="https://zhengyushan.github.io" target="_blank">Yushan Zheng*</a>, Fengying Xie, Zhiguo Jiang, Jun Shi
+                </i></font>
+                <br>
+                  IEEE International Conference on Bioinformatics and Bioengineering (BIBE), 2022
+                <br>
+                <!-- <i class="fa fa-file-pdf-o"></i> <a href="https://zhengyushan.github.io" target="_blank">PDF</a> -->
+                <i class="fa fa-bookmark-o"></i> <a href="javascript:toggleblock('ZhengBIBE2022Abs')">Abstract</a> &nbsp;
+                <i class="fa fa-quote-left"></i> <a href="javascript:toggleblock('ZhengBIBE2022Bib')">BibTeX</a> &nbsp;
+                <i class="fa fa-github"></i> <a href="" target="_blank">Code</a>
+            </p>
+            <p id="ZhengBIBE2022Abs" class="abstract" style="display: none;">
+                Computer-aided cancer diagnosis (CAD) methods based on the histopathological images have achieved great development. The content-based whole slide image (WSI) retrieval is one of the important application that can search for the informative data to assist clinical diagnosis. It is notable that the current retrieval system are mainly developed based on the image content and image labels. The diagnosis report for the WSIs given by the pathologists are also valuable data, but have not yet been adequately considered in modeling. In this paper, we propose a cross-modal retrieval framework based on histopathology WSIs and diagnosis report, which can simultaneously achieve four retrieval tasks for histopathology database across WSIs and diagnosis reports. The compact binary features from both WSIs and diagnosis reports are first extracted, and then built in a common vision-language semantic feature space by the constraint of the designed cross hashing loss function. The method was verified on a gastric histopathology dataset that contains 932 gastric cases with 4 lesion categories. Experimental results have demonstrated the effectiveness of the proposed method in the cross-modal retrieval tasks for digital pathology system.
+            </p>
+            <pre xml:space="preserve" id="ZhengBIBE2022Bib" class="bibtex" style="display: none;">
+@INPROCEEDINGS{9973633,
+  author={Hu, Dingyi and Xie, Fengying and Jiang, Zhiguo and Zheng, Yushan and Shi, Jun},
+  booktitle={2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE)}, 
+  title={Histopathology Cross-Modal Retrieval based on Dual-Transformer Network}, 
+  year={2022},
+  volume={},
+  number={},
+  pages={97-102},
+  keywords={Solid modeling;Design automation;Histopathology;Databases;Semantics;Feature extraction;Data models;Histopathology image analysis;Cross-modal retrieval;CAD},
+  doi={10.1109/BIBE55377.2022.00028}}
             </pre>
             <script language="javascript" type="text/javascript" xml:space="preserve">
               hideblock('ZhengMICCAI2022Abs');