ROI suppresor on LIDC preprocessing.py about medicaldetectiontoolkit

[ivanwilliammd]

Update: I already able to disable the RoI suppressor by modifying extra zero numpy array made.
However I still struggle with problem number 2

[pfjaeger]

hi the preprocessing script is only an example script for the LIDC data set! For your custom data set you would need to write your own custom preprocessing script. The example script can help you to get an idea how to structure your script but the exact steps such as loading of your data need to be customized according to your specific needs.

Features like aggregation over multiple raters are very specific to LIDC and you do not need to do that so you can probably skip half of the steps in the example script.

[ivanwilliammd]

hi the preprocessing script is only an example script for the LIDC data set! For your custom data set you would need to write your own custom preprocessing script. The example script can help you to get an idea how to structure your script but the exact steps such as loading of your data need to be customized according to your specific needs.

Features like aggregation over multiple raters are very specific to LIDC and you do not need to do that so you can probably skip half of the steps in the example script.

Thank you Sir, I already create my custom dataset, however sometimes when running preprocessing.py, there are some error Index 0 is out of bound for axis 0 with size 0 which resulted in me deleting the data. Is the problem lie on the csv or the image file Sir?

[ivanwilliammd]

hi the preprocessing script is only an example script for the LIDC data set! For your custom data set you would need to write your own custom preprocessing script. The example script can help you to get an idea how to structure your script but the exact steps such as loading of your data need to be customized according to your specific needs.

Features like aggregation over multiple raters are very specific to LIDC and you do not need to do that so you can probably skip half of the steps in the example script.

By the way, can I ask for reference how much epoch, batch_size, and num_train_batches suitable for training around 300-500 thorax CT scan dataset (approximately each file has similar file size with LIDC dataset will be run at Tesla P100 16GB)?

[pfjaeger]

I would recommend you to maximize batch size filling up GPU memory. Pick an intuitive number for epochs and training batches and observe in the monitoring plot if your model is overfitting and then adjust the training length accordingly.

[ivanwilliammd]

I would recommend you to maximize batch size filling up GPU memory. Pick an intuitive number for epochs and training batches and observe in the monitoring plot if your model is overfitting and then adjust the training length accordingly.

Thank you Sir for the tips. By the way, I already done some quick train (only folds_0) with parameter as following: #40

with configs.py:

        self.report_score_level = ['patient', 'rois']  # choose list from 'patient', 'rois'
        self.class_dict = {1: 'groundglass', 2: 'subsolid', 3: 'solid'}  # 0 is background.
        self.patient_class_of_interest = 2  # patient metrics are only plotted for one class.
        self.ap_match_ious = [0.1]  # list of ious to be evaluated for ap-scoring.

        self.model_selection_criteria = ['solid_ap', 'subsolid_ap', 'groundglass_ap'] # criteria to average over for saving epochs.
        self.min_det_thresh = 0.1  # minimum confidence value to select predictions for evaluation.
        self.head_classes = 4

        # seg_classes hier refers to the first stage classifier (RPN)
        self.num_seg_classes = 2  # foreground vs. background

and the results I get :

****************************
results for fold 0 
****************************
fold df shape (12551, 7)
  
AUC 0.5000  AP 0.5061 fold_0 patient cl_1 
AUC 0.0000  AP 0.0000 fold_0 rois cl_1 
AUC 0.5000  AP 0.3882 fold_0 patient cl_2 
AUC 0.0000  AP 0.0000 fold_0 rois cl_2 
AUC 0.1154  AP 0.7575 fold_0 patient cl_3 
AUC 0.0000  AP 0.0000 fold_0 rois cl_3 
AUC 0.0000  AP 0.0000 average_foreground_roi 

If I'm not mistaken AUC 0.5 means it can't differentiate the class clearly, and rois cl_1, 2, 3 equal to zero means that all the prediction boxes missed?
Visualization show that the anchors already OK, classification too, however the bounding boxes are off.

Are the modified configs.py parameters applicable for 3 class texture classification (solid, subsolid, groundglass)?
Thank you Sir

[pfjaeger]

hi can you please post this in the slack channel? i guess this is getting a little off topic for the initial issue. thanks!

[ivanwilliammd]

hi can you please post this in the slack channel? i guess this is getting a little off topic for the initial issue. thanks!

Okay Sir.