Update publications.html

publications.html

@@ -480,7 +480,7 @@ <h3>2023</h3>
                 <!-- <i class="fa fa-file-pdf-o"></i> <a href="" target="_blank">PDF</a> -->
                 <i class="fa fa-bookmark-o"></i> <a href="javascript:toggleblock('XieMICCAI2023Abs')">Abstract</a> &nbsp;
                 <i class="fa fa-quote-left"></i> <a href="javascript:toggleblock('XieMICCAI2023Bib')">BibTeX</a> &nbsp;
-                <!-- <i class="fa fa-github"></i> <a href="https://github.com/zylbuaa/ECL.git" target="_blank">Code</a> -->
+                <!-- <i class="fa fa-github"></i> <a href="" target="_blank">Code</a> -->
             </p>
             <p id="XieMICCAI2023Abs" class="abstract" style="display: none;">
               Skin image datasets often suffer from imbalanced data distribution, exacerbating the difficulty of computer-aided skin disease diagnosis. Some recent works exploit supervised contrastive learning (SCL) for this long-tailed challenge. Despite achieving significant performance, these SCL-based methods focus more on head classes, yet ignoring the utilization of information in tail classes. In this paper, we propose class-Enhancement Contrastive Learning (ECL), which enriches the information of minority classes and treats different classes equally. For information enhancement, we design a hybrid-proxy model to generate class-dependent proxies and propose a cycle update strategy for parameters optimization. A balanced-hybrid-proxy loss is designed to exploit relations between samples and proxies with different classes treated equally. Taking both “imbalanced data” and “imbalanced diagnosis difficulty” into account, we further present a balanced-weighted cross-entropy loss following curriculum learning schedule. Experimental results on the classification of imbalanced skin lesion data have demonstrated the superiority and effectiveness of our method. The codes can be publicly available from https://github.com/zylbuaa/ECL.git.