modifying the series list

app.py

@@ -12,11 +12,13 @@ import papermill as pm
 import subprocess 
 from PIL import Image
 
-st.write(os.listdir())
+# st.write(os.listdir())
 
 # Main Streamlit app code
 st.title("DICOM Classification Demo")
 st.write("Select IDC data to download, extract images and metadata, and perform inference using three pre-trained models")
+st.write("NOTE: This demo only works for classification of MR series of the prostate - T2 weighted axial, DWI, ADC and DCE")
+st.write("NOTE: These models were trained on patients from QIN-Prostate-Repeatability PCAMPMRI-00001 to PCAMPMRI-00012 and on ProstateX ProstateX-0000 to ProstateX-0275 patients. Therefore it is wise to not evaluate on those patients." )
 
 # Fetch IDC index
 client = index.IDCClient()
@@ -25,6 +27,9 @@ index_df = client.index
 # Option to choose IDC data
 st.subheader("Choose IDC Data to Process")
 collection_ids = index_df["collection_id"].unique()
+# only keep collection_ids with prostate in the name 
+collection_ids = [f if "prostate" in f for f in collection_ids]
+print('collection_ids: ' + str(collection_ids))
 selected_collection_id = st.selectbox("Select Collection ID", collection_ids)
 
 # Filter dataframe based on selected collection_id
@@ -49,6 +54,13 @@ selected_study = st.selectbox("Select Study", studies)
 df_filtered_by_study = df_filtered_by_modality[df_filtered_by_modality["StudyInstanceUID"] == selected_study]
 
 series = df_filtered_by_study["SeriesInstanceUID"].unique()
+# Get the corresponding list of SeriesDescriptions 
+series_descriptions = df_filtered_by_study[df_filtered_by_study['SeriesInstanceUID'].isin(series)]['SeriesDescription'].values 
+
+print('number of series: ' + str(len(series)))
+print('series_descriptions: ' + str(series_descriptions))
+print('number of series_descriptions: ' + str(len(series_descriptions)))
+
 selected_series = st.selectbox("Select Series", series)
 
 print('selected_series: ' + str(selected_series))
@@ -57,7 +69,7 @@ print('selected_series: ' + str(selected_series))
 if st.button("Run inference"):
 
     # Code to run when the button is pressed
-    st.write("Button pressed! Running inference")
+    st.write("Button pressed! Running inference using three models")
 
     if not os.path.exists("DICOMScanClassification_user_demo.ipynb"):
         subprocess.run(["wget", "https://raw.githubusercontent.com/deepakri201/DICOMScanClassification_pw41/main/DICOMScanClassification_user_demo.ipynb"])