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@@ -42,10 +42,17 @@ <h1> COBRA: Confidence-based Anomaly Detection for Medicine </h1>
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 										<p>The paper presents a method to perform automatic assessment of impairment and disease severity using AI models trained only on healthy individuals. The <strong>COnfidence-Based chaRacterization of Anomalies (COBRA)</strong> score exploits the decrease in confidence of these models when processing data from impaired or diseased patients to quantify their deviation from the healthy population.</p>
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-										<span class="image fit"><img src="images/COBRA-Overview-Stroke.png" alt="" /></span>
-										<span class="image fit"><img src="images/COBRA-Overview-KneeOA.png" alt="" /></span>
+										<p>This diagram explains how the method quantifies impairment in stroke patients (left) and disease severity of knee osteoarthritis (right) :</p>
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+												<span class="image fit"><img src="images/COBRA-Overview-Stroke.png" alt="" /></span>
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+												<span class="image fit"><img src="images/COBRA-Overview-KneeOA.png" alt="" /></span>
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 										<p>The plots below compare the COBRA score against expert-based metrics: (a) Fugl-Meyer assessment (FMA) score for impairment quantification in stroke patients, (b) Kellgren-Lawrence (KL) grade for knee impairment. The COBRA score computed automatically in under a minute is strongly correlated with the expert-based metrics.</p>
 
@@ -72,7 +79,7 @@ <h2>Data</h2>
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