Update README.md

Author: ttanida

README.md

@@ -19,12 +19,12 @@ The automatic generation of radiology reports has the potential to assist radiol
 <p align="center">
   <img src="figures_repo/full_report_generation.png" alt="Full report generation" width="30%">
 </p>
-<p align="center">Figure 2. <b>Full report generation</b> for a test set image. Detected anatomical regions (solid boxes), corresponding generated sentences, and semantically matching reference sentences are colored the same. The generated report mostly captures the information contained in the reference report, as reflected by the matching colors.</p>
+<p align="left">Figure 2. <b>Full report generation</b> for a test set image. Detected anatomical regions (solid boxes), corresponding generated sentences, and semantically matching reference sentences are colored the same. The generated report mostly captures the information contained in the reference report, as reflected by the matching colors.</p>
 
 <p align="center">
   <img src="figures_repo/anatomy_based_sentence_generation.png" alt="Anatomy-based generation" width="60%">
 </p>
-<p align="center">Figure 3. Interactive capability of <b>anatomy-based sentence generation</b> allows for the generation of descriptions for anatomical regions explicitly chosen by the radiologist. We show predicted (dashed boxes) and ground-truth (solid boxes) anatomical regions and color sentences accordingly. We observe that the model generates pertinent, anatomy-related sentences.</p>
+<p align="left">Figure 3. Interactive capability of <b>anatomy-based sentence generation</b> allows for the generation of descriptions for anatomical regions explicitly chosen by the radiologist. We show predicted (dashed boxes) and ground-truth (solid boxes) anatomical regions and color sentences accordingly. We observe that the model generates pertinent, anatomy-related sentences.</p>
 
 ## Setup