MICCAI

constants.py

@@ -1,7 +1,7 @@
 NEURIPS_ICO = "data:image/png;base64,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"
 COLM_ICO = "data:image/svg+xml;base64,PCEtLSBmYXZpY29uLnN2ZyAtLT4KPHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPgogIDx0ZXh0IHk9IjMyIiBmb250LXNpemU9IjMyIj7wn6aZPC90ZXh0Pgo8L3N2Zz4="
 DEFAULT_ICO = NEURIPS_ICO
-
+MICCAI24ICO = "data:image/png;base64,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"
 DATASET_ARXIV_SCAN_PAPERS = "IAMJB/scanned-arxiv-papers-id"
 DATASET_CONFERENCE_PAPERS = "IAMJB/paper_conference_aggregate"
 DATASET_DAILY_PAPERS = "hysts-bot-data/daily-papers"

df/PaperCentral.py

@@ -8,6 +8,7 @@ from constants import (
     DATASET_PAPER_CENTRAL,
     COLM_ICO,
     DEFAULT_ICO,
+    MICCAI24ICO,
 )
 import gradio as gr
 from utils import load_and_process
@@ -31,6 +32,7 @@ class PaperCentral:
         "NeurIPS2023",
         "NeurIPS2023 D&B",
         "COLM2024",
+        "MICCAI2024",
     ]
     CONFERENCES_ICONS = {
         "ACL2023": 'https://aclanthology.org/aclicon.ico',
@@ -44,6 +46,7 @@ class PaperCentral:
         "NeurIPS2023": NEURIPS_ICO,
         "NeurIPS2023 D&B": NEURIPS_ICO,
         "COLM2024": COLM_ICO,
+        "MICCAI2024": MICCAI24ICO,
     }
 
     # Class-level constants defining columns and their data types
@@ -251,12 +254,6 @@ class PaperCentral:
 
         df = df.copy()
 
-        # Sort rows to display entries with 'paper_page' first
-        if 'paper_page' in df.columns:
-            df['has_paper_page'] = df['paper_page'].notna()
-            df.sort_values(by='has_paper_page', ascending=False, inplace=True)
-            df.drop(columns='has_paper_page', inplace=True)
-
         # Apply the update_row function to each row
         prettified_df: pd.DataFrame = df.apply(update_row, axis=1)
         return prettified_df
@@ -451,6 +448,12 @@ class PaperCentral:
             PaperCentral.DATATYPES.get(self._get_original_column_name(col), 'str') for col in filtered_df.columns
         ]
 
+        # Sort rows to display entries with 'paper_page' first
+        if 'paper_page' in filtered_df.columns:
+            filtered_df['has_paper_page'] = filtered_df['paper_page'].notna() & (filtered_df['paper_page'] != "")
+            filtered_df.sort_values(by='has_paper_page', ascending=False, inplace=True)
+            filtered_df.drop(columns='has_paper_page', inplace=True)
+
         # Return an update object to modify the Dataframe component
         return gr.update(value=filtered_df, datatype=new_datatypes)