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MICV-yonsei · Jun 4, 2024 · 1cfa502 · 1cfa502
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@@ -196,6 +196,13 @@ <h4 class="title is-4 has-text-centered">Pipeline</h4>
           <p>
             The pipeline of <i>CXRL</i>. 
             Our model employs policy gradient optimization utilizing multi-reward feedback, fine-tuning image generator, and ACE to produce realistic and accurate CXR that corresponds closely to the input report.
+            <b>Contribution</b>
+            <br>
+            <ul>
+              <li>Our study pioneers in applying RL to text-conditioned medical image synthesis, particularly in CXRs, focusing on detail refinement and input condition control for clinical accuracy.</li>
+              <li>We advance report-to-CXR generation with an RLCF-based rewarding mechanism, emphasizing posture alignment, pathology accuracy, and consistency between input reports and generated CXRs.</li>
+              <li>We jointly optimize the image generator and ACE via reward feedback models, ensuring image-text alignment and medical accuracy across varied reports, setting a new benchmark in a report-to-CXR generation.</li>
+            </ul>
           </p>
         </div>