Upload 4_🔬_Decision_Exploration_Panel.py

pages/4_🔬_Decision_Exploration_Panel.py

@@ -23,16 +23,22 @@ backend = st.sidebar.selectbox(
 
 st.session_state['user_group'] = backend
 
-st.title("DecisionExploration Panel for OCT Image Analysis")
+st.title("Decision Exploration Panel for OCT Image Analysis")
 st.write(
         """
-       This panel provides information on the evaluation of the AI model’s performance, including details on the metrics used and the results of the evaluation. USerrs can also find 
-       our notes regarding the issues. The performance metric visualizations and samples of failure and success cases are given in in this panel as well.""")
+       This panel provides provides both local and global explanations.
+       """)
 
 list_test = """<ul>
-                    <li>Evaluation Metrics: This tab explains the details of the performance metrics and how each metric is calculated. 
-                        Users can also find the characteristics of the evaluation data set. </li>
-                </ul>"""
+                <li>Global Explanations: Trepresentative samples and task-specific borderline samples from the training data, such as representative
+                samples from each category in a classification task. These samples enable users
+                to understand which inputs contribute to the model’s decisions. On the other
+                hand, borderline cases can be used to highlight potential failure cases. Also, once can see manifold visualization
+                of both representative and borderline samples </li>
+                <li>Instance Level Explanations: Tthis panel is intended for model sensitivity analyis, decision correction, and decision highlighting using both local expla-
+                nation methods and presenting similar samples to the target sample from the
+                representative samples of the training set.</li>
+                    </ul>"""
 st.markdown(list_test, unsafe_allow_html=True)
 
 if backend == "Developer":