update plot and table

wanshi/add_contrast.py

@@ -0,0 +1,27 @@
+import os
+import cv2
+
+def enhance_contrast(image_path, output_path, alpha, beta):
+    # Open the image file
+    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)  # Convert image to grayscale
+
+    # Enhance Image Contrast
+    image_enhanced = cv2.convertScaleAbs(image, alpha=alpha, beta=beta)
+
+    # Save the enhanced image
+    cv2.imwrite(output_path, image_enhanced)
+
+# Set the directory you want to start from
+rootDir = '/home/jeff/test_slices'
+for dirName, subdirList, fileList in os.walk(rootDir):
+    print('Found directory: %s' % dirName)
+    for fname in fileList:
+        if fname.endswith('.jpg'):
+            print('\t%s' % fname)
+            input_path = os.path.join(dirName, fname)
+            #mkdir if not exist
+            if not os.path.exists(os.path.join(dirName, 'enhanced')):
+                os.mkdir(os.path.join(dirName, 'enhanced'))
+            output_path = os.path.join(dirName, 'enhanced', fname)
+            print(output_path)
+            enhance_contrast(input_path, output_path, 3, 0)

wanshi/enhance_edge.py

@@ -0,0 +1,29 @@
+import os
+import cv2
+
+def enhance_edges(image_path, output_path, low_threshold, high_threshold):
+    # Open the image file
+    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)  # Convert image to grayscale
+
+    # Apply Gaussian blur to reduce noise
+    image_blur = cv2.GaussianBlur(image, (5, 5), 0)
+
+    # Apply Canny Edge Detection
+    edges = cv2.Canny(image_blur, low_threshold, high_threshold)
+
+    # Save the enhanced image
+    cv2.imwrite(output_path, edges)
+
+# Set the directory you want to start from
+rootDir = '/home/jeff/test_slices'
+for dirName, subdirList, fileList in os.walk(rootDir):
+    print('Found directory: %s' % dirName)
+    for fname in fileList:
+        if fname.endswith('.jpg'):
+            print('\t%s' % fname)
+            input_path = os.path.join(dirName, fname)
+            if not os.path.exists(os.path.join(dirName, 'enhanced_edge')):
+                os.mkdir(os.path.join(dirName, 'enhanced_edge'))
+            output_path = os.path.join(dirName, 'enhanced_edge', fname)
+            print(output_path)
+            enhance_edges(input_path, output_path, 100, 200)  # Adjust thresholds as needed

wanshi/visualizations/crona/META-AI_ Guideline Items - Tabellenblatt_updated.csv

@@ -0,0 +1,47 @@
+,Guideline Item,Description (specification and/or example content),Item type,AI-specific item?,STARE-HI,TRIPOD,Luo et al.,CONSORT-AI,SPIRIT-AI,Schwendicke et al.,CLEAR Derm,DECIDE-AI,CLEAR,,% Item included in high-level consensus guidelines (Y/P),,Good ML Practice,MI-CLAIM,PRIME,DOME,Shen,Hatt et al.,,% Item included in intermediate-level consensus guidelines (Y/P),,Vihinen,CLAIM,MINIMAR,Stevens et al.,CAIR,PIECES,Zukotynski et al.,El Naqa et al.,Jones et al.,R-AI-DIOLOGY,Volovici et al.,,% Item included in low-level consensus guidelines (Y/P),,% Item included in all guidelines (Y/P),,,General,,Specific
+Year,,,,,2009,2015,2016,2020,2020,2021,2022,2022,2023,,,,2019,2020,2020,2021,2022,2023,,,,2012,2020,2020,2020,2021,2021,2021,2021,2022,2022,2022,,,,,,,,,
+Inclusion Process,,,,,"""+""",%,*,*,*,*,*,*,*,,,,"""+""",*,*,*,*,#,,,,*,#,*,*,*,#,*,#,*,*,#,,,,,,,,,
+Guideline Type,,,,,G,G,G,G,G,S,S,G,S,,,,G,G,S,G,S,S,,,,G,S,G,G,G,S,S,S,S,S,G,,,,,,,,,
+Level of Consensus,,,,,H,H,H,H,H,H,H,H,H,,,,M,M,M,M,M,M,,,,L,L,L,L,L,L,L,L,L,L,L,,,,,,,,,
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Clinical Rationale,Topic,Predictive AI,Content,Yes,N,Y,Y,Y,Y,Y,N,Y,Y,,"0,78",,N,N,P,N,N,N,,"0,17",,N,Y,N,N,Y,N,N,N,Y,N,N,,"0,27",,"0,42",,,"0,43",,"0,50"
+,Study Design,"Retrospective vs. prospective, prognostic vs. diagnostic",Content,Partially,Y,Y,Y,N,N,Y,N,Y,Y,,"0,67",,N,Y,Y,N,N,Y,,"0,50",,N,Y,N,N,N,N,N,N,N,N,N,,"0,09",,"0,38",,,"0,36",,"0,50"
+,Prediction Problem,"Prediction target, outcome parameters, performance metrics",Content,Yes,Y,Y,Y,Y,Y,Y,N,Y,Y,,"0,89",,P,Y,Y,N,N,Y,,"0,67",,N,Y,P,P,P,Y,N,N,Y,P,N,,"0,64",,"0,73",,,"0,79",,"0,75"
+,Clinical Setting,Details on the clinical problem and intended use,Content,No,Y,Y,Y,Y,Y,Y,Y,Y,Y,,"1,00",,Y,Y,N,N,P,Y,,"0,67",,N,Y,P,P,Y,Y,N,P,Y,Y,N,,"0,73",,"0,81",,,"0,79",,"0,83"
+,Rationale,Relation between prediction problem and clinical goal,Content,Yes,Y,P,Y,Y,Y,P,P,Y,P,,"1,00",,Y,Y,N,N,N,Y,,"0,50",,N,Y,N,P,P,P,N,P,Y,P,N,,"0,64",,"0,73",,,"0,71",,"0,75"
+,Existing AI and Statistical Models,"Performance metrics, level of translation, clinical application",Content,Yes,P,Y,Y,N,Y,Y,N,P,Y,,"0,78",,N,P,Y,Y,N,N,,"0,50",,Y,N,N,N,N,P,N,P,P,P,Y,,"0,55",,"0,62",,,"0,64",,"0,58"
+,State-of-the-art,Identify state-of-the-art clinical solution and use as a baseline for comparison,Quality,Partially,P,P,N,N,N,P,N,N,P,,"0,44",,P,Y,N,Y,P,N,,"0,67",,P,N,N,N,N,N,N,N,N,N,N,,"0,09",,"0,35",,,"0,43",,"0,33"
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Data,"Data Sources, Types, and Structure","Original data format and volume, facility details, structured vs. unstructured data",Content,Partially,Y,Y,Y,P,P,Y,Y,Y,Y,,"1,00",,P,Y,Y,P,P,Y,,"1,00",,N,Y,Y,Y,Y,Y,Y,Y,Y,Y,Y,,"0,91",,"0,96",,,"0,93",,"1,00"
+,Data Selection,Inclusion and exclusion criteria at the level of data and participants,Content,Partially,Y,Y,Y,Y,Y,Y,N,Y,Y,,"0,89",,P,N,N,N,P,P,,"0,50",,N,Y,Y,N,Y,Y,N,N,Y,N,Y,,"0,55",,"0,65",,,"0,71",,"0,67"
+,Data Preprocessing,"Data transformation, handling of missing data and outliers",Content,Yes,Y,Y,Y,Y,Y,Y,Y,Y,Y,,"1,00",,P,Y,Y,N,P,Y,,"0,83",,N,Y,N,Y,Y,Y,N,Y,P,Y,Y,,"0,73",,"0,85",,,"0,79",,"0,92"
+,Labeling of Input Data,"Clinical outcome vs. expert rating, number and expertise of labellers","Content, Quality",Partially,Y,N,N,Y,Y,Y,P,Y,Y,,"0,78",,Y,N,N,N,P,Y,,"0,50",,N,Y,P,P,P,Y,N,Y,Y,N,P,,"0,73",,"0,69",,,"0,64",,"0,75"
+,Rater Variability,Inter- and intrarater variability,Quality,Partially,Y,N,N,N,N,Y,N,N,Y,,"0,33",,N,N,N,N,N,N,,"0,00",,N,Y,N,N,Y,N,N,P,N,N,N,,"0,27",,"0,23",,,"0,14",,"0,33"
+,Data Processing Location,"Specification of data processing location (local vs. cloud, external institutions involved in data processing, data flow)",Content,Partially,N,N,N,N,N,Y,N,N,N,,"0,11",,N,N,N,N,Y,N,,"0,17",,N,N,N,N,N,N,N,N,N,Y,N,,"0,09",,"0,12",,,"0,00",,"0,33"
+,De-Identification,Address anonymization/de-identification of data,Quality,Partially,N,N,N,N,N,Y,N,N,Y,,"0,22",,N,N,N,N,Y,N,,"0,17",,N,Y,N,N,N,Y,N,N,N,Y,N,,"0,27",,"0,23",,,"0,00",,"0,58"
+,Data Dictionary,Release data dictionary with explanations of variables,Content,Partially,N,N,N,N,N,N,N,N,N,,"0,00",,P,N,Y,N,N,N,,"0,33",,N,Y,N,N,N,N,N,N,N,N,N,,"0,09",,"0,12",,,"0,07",,"0,25"
+,Data Leakage,"Independence of training/validation/test data (i.e. do not use evaluation sets for feature selection, preprocessing steps or parameter tuning)",Quality,Yes,N,N,N,N,N,N,N,N,P,,"0,11",,Y,Y,N,Y,N,Y,,"0,67",,Y,N,N,P,N,Y,N,P,Y,N,N,,"0,45",,"0,38",,,"0,36",,"0,42"
+,Representativeness,Training and test data should be representative of real-world clinical settings,Quality,Yes,P,P,N,N,N,Y,N,N,N,,"0,33",,Y,Y,N,Y,P,P,,"0,83",,N,N,N,N,N,N,N,Y,Y,P,Y,,"0,36",,"0,46",,,"0,43",,"0,50"
+,Basic Statistics of the Dataset,Distribution of input and outcomes,Content,Partially,Y,Y,Y,N,N,N,Y,N,N,,"0,44",,Y,N,Y,Y,N,N,,"0,50",,N,Y,Y,N,N,N,N,Y,Y,N,Y,,"0,45",,"0,46",,,"0,50",,"0,42"
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Model Training and Validation,Type of Prediction Model,"Type of algorithm, classification vs. regression",Content,Yes,N,Y,Y,Y,Y,Y,N,Y,Y,,"0,78",,N,P,Y,Y,N,N,,"0,50",,N,Y,Y,N,Y,Y,Y,Y,Y,N,N,,"0,64",,"0,65",,,"0,64",,"0,67"
+,Model Development,"Identification and removal of redundant independent variables, model training and selection strategy",Content,Yes,N,Y,Y,N,P,Y,Y,P,Y,,"0,78",,P,Y,Y,Y,P,P,,"1,00",,Y,Y,Y,Y,Y,Y,P,Y,Y,P,P,,"1,00",,"0,92",,,"0,86",,"1,00"
+,Model Validation,"Internal vs. external vs. cross validation, validation metrics",Content,Yes,Y,Y,Y,N,N,Y,P,P,Y,,"0,78",,P,P,Y,Y,N,P,,"0,83",,Y,Y,P,Y,Y,Y,Y,Y,Y,P,P,,"1,00",,"0,88",,,"0,86",,"0,92"
+,Model Interpretability,Statement on model interpretability,"Content, Quality",Yes,N,N,Y,N,N,Y,N,N,Y,,"0,33",,Y,N,Y,Y,N,N,,"0,50",,N,Y,N,P,N,N,N,P,Y,N,Y,,"0,45",,"0,42",,,"0,36",,"0,50"
+,Model Performance and Interpretation,"Outcome metrics, confidence intervals",Content,Yes,Y,Y,Y,P,P,P,Y,Y,Y,,"1,00",,Y,P,P,Y,P,N,,"0,83",,Y,Y,Y,Y,P,Y,N,Y,Y,P,P,,"0,91",,"0,92",,,"1,00",,"0,83"
+,Computational Cost,"Model execution time, floating point operations per second",Content,Yes,N,N,N,N,N,N,N,N,N,,"0,00",,N,N,N,Y,N,N,,"0,17",,N,N,N,N,N,N,Y,P,N,N,N,,"0,18",,"0,12",,,"0,07",,"0,17"
+,Statistical Methods,Appropriate methods and significance levels for performance comparison of baseline and proposed model,Quality,Partially,Y,Y,N,N,N,Y,N,N,Y,,"0,44",,Y,Y,N,Y,N,N,,"0,50",,P,Y,N,N,N,P,N,Y,Y,N,Y,,"0,55",,"0,50",,,"0,50",,"0,50"
+,Performance Errors,Identification and analysis of errors,"Content, Quality",Yes,Y,Y,N,Y,Y,N,P,Y,P,,"0,78",,Y,N,P,N,P,N,,"0,50",,P,Y,N,N,Y,N,N,N,N,N,P,,"0,36",,"0,54",,,"0,64",,"0,50"
+,Over-/Underfitting,Assessment of the possibility of over-/underfitting (i.e. by reporting indicators such as train vs. test error),Quality,Yes,N,N,N,N,N,N,N,N,N,,"0,00",,Y,N,N,Y,N,N,,"0,33",,P,N,N,P,N,N,N,Y,N,N,N,,"0,27",,"0,19",,,"0,29",,"0,08"
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Critical Appraisal,Clinical Implications and Practical Value,"Potential augmentations of clinical workflows, potential changes in clinical decision making",Content,Partially,Y,Y,Y,Y,Y,Y,Y,Y,Y,,"1,00",,Y,N,N,N,Y,N,,"0,33",,N,Y,N,N,N,N,N,Y,N,Y,N,,"0,27",,"0,54",,,"0,50",,"0,58"
+,Translation,Details on integration into clinical workflow,Content,Partially,N,Y,N,N,N,N,N,Y,N,,"0,22",,Y,N,N,N,P,N,,"0,33",,N,Y,N,N,N,N,N,N,Y,Y,N,,"0,27",,"0,27",,,"0,21",,"0,42"
+,Limitations,"Bias, generalizability, interpretation pitfalls",Content,Partially,Y,Y,Y,N,N,P,Y,Y,Y,,"0,78",,Y,N,Y,N,P,N,,"0,50",,N,Y,N,N,N,N,N,Y,P,N,Y,,"0,36",,"0,54",,,"0,43",,"0,67"
+,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+Ethics and Reproducibility,Data Publication,Publication of datasets or inclusion of a statement on public availability,Content,Partially,N,Y,N,N,N,Y,N,Y,Y,,"0,44",,N,Y,N,Y,N,N,,"0,33",,Y,N,Y,Y,Y,N,N,N,Y,N,Y,,"0,55",,"0,46",,,"0,64",,"0,33"
+,Code Publication,Publication of code or inclusion of a statement on public availability,Content,Yes,N,Y,N,Y,Y,Y,N,Y,Y,,"0,67",,N,Y,Y,Y,N,N,,"0,50",,N,N,Y,Y,Y,N,P,Y,N,N,Y,,"0,55",,"0,58",,,"0,71",,"0,42"
+,AI Intervention Publication,Publication of AI Intervention or inclusion of a statement on public availability,Content,Yes,P,N,N,Y,Y,N,N,N,Y,,"0,44",,N,Y,N,Y,N,N,,"0,33",,N,N,N,N,N,N,N,N,N,N,N,,"0,00",,"0,23",,,"0,36",,"0,17"
+,Future Updates,Details on future software/algorithm updates (i.e. how users will be informed),Content,Partially,N,N,N,N,N,N,N,N,N,,"0,00",,P,N,N,N,P,N,,"0,33",,N,N,N,N,N,P,N,N,Y,Y,N,,"0,27",,"0,19",,,"0,07",,"0,42"
+,Ethical Statement,Details on IRB approval and informed consent procedure,Content,No,Y,Y,Y,N,N,Y,N,Y,Y,,"0,67",,N,N,N,N,Y,N,,"0,17",,N,N,N,N,Y,N,N,Y,N,N,N,,"0,18",,"0,35",,,"0,36",,"0,33"
+,Equity and Access,"Statement on equity, diversity and access to AI application","Content, Quality",Yes,N,N,N,N,N,P,N,P,P,,"0,33",,P,N,N,N,Y,N,,"0,33",,N,N,N,N,N,N,N,N,N,N,N,,"0,00",,"0,19",,,"0,14",,"0,33"
+,Legal and Regulatory Aspects,Statement on legal and regulatory aspects,Content,Partially,N,N,N,N,N,Y,N,N,N,,"0,11",,N,N,N,N,Y,N,,"0,17",,N,N,N,N,N,N,N,N,Y,Y,N,,"0,18",,"0,15",,,"0,00",,"0,42"

wanshi/visualizations/crona/all.csv

@@ -0,0 +1,38 @@
+group,Guideline Item,% Item included in high-level consensus guidelines (Y/P),% Item included in intermediate-level consensus guidelines (Y/P),% Item included in low-level consensus guidelines (Y/P),% Item included in all guidelines (Y/P)
+Clinical Rationale,Topic,"0,75","0,17","0,29","0,43"
+,Study Design,"0,63","0,50","0,14","0,43"
+,Prediction Problem,"0,88","0,67","0,57","0,71"
+,Clinical Setting,"1,00","0,67","0,57","0,76"
+,Rationale,"1,00","0,50","0,57","0,71"
+,Existing AI and Statistical Models,"0,75","0,50","0,71","0,67"
+,State-of-the-art,"0,50","0,67","0,14","0,43"
+Data,"Data Sources Types and Structure","1,00","1,00","0,86","0,95"
+,Data Selection,"0,88","0,50","0,57","0,67"
+,Data Preprocessing,"1,00","0,83","0,71","0,86"
+,Labeling of Input Data,"0,75","0,50","0,57","0,62"
+,Rater Variability,"0,25","0,00","0,14","0,14"
+,Data Processing Location,"0,13","0,17","0,14","0,14"
+,De-Identification,"0,13","0,17","0,43","0,24"
+,Data Dictionary,"0,13","0,33","0,14","0,19"
+,Data Leakage,"0,13","0,67","0,43","0,38"
+,Representativeness,"0,38","0,83","0,43","0,52"
+,Basic Statistics of the Dataset,"0,75","0,50","0,43","0,57"
+Model Training and Validation,Type of Prediction Model,"0,75","0,50","0,57","0,62"
+,Model Development,"0,88","1,00","1,00","0,95"
+,Model Validation,"0,75","0,83","1,00","0,86"
+,Model Interpretability,"0,25","0,50","0,43","0,38"
+,Model Performance and Interpretation,"1,00","0,83","0,86","0,90"
+,Computational Cost,"0,13","0,17","0,14","0,14"
+,Statistical Methods,"0,38","0,50","0,71","0,52"
+,Performance Errors,"0,88","0,50","0,43","0,62"
+,Over-/Underfitting,"0,13","0,33","0,14","0,19"
+Critical Appraisal,Clinical Implications and Practical Value,"1,00","0,33","0,29","0,57"
+,Translation,"0,38","0,33","0,43","0,38"
+,Limitations,"1,00","0,50","0,43","0,67"
+Ethics and Reproducibility,Data Publication,"0,38","0,33","0,43","0,38"
+,Code Publication,"0,63","0,50","0,29","0,48"
+,AI Intervention Publication,"0,50","0,33","0,00","0,29"
+,Future Updates,"0,50","0,33","0,43","0,43"
+,Ethical Statement,"0,63","0,17","0,00","0,29"
+,Equity and Access,"0,25","0,33","0,00","0,19"
+,Legal and Regulatory Aspects,"0,13","0,17","0,29","0,19"