Val Loss increases

[csccsccsccsc]

When I train HACT-Net (or the cell graph / tissue graph net), the validation loss keeps increasing and the accuracy keeps unchanged (very low).

[guillaumejaume]

I would need more information to help you here,

• Are you re-training on the BRACS dataset?, If yes, BRACSv1 or BRACSv2 (cf. README, Section: Downloading the BRACS dataset)
• Have you tried to use a pre-trained model to see if you can reproduce the results?
• Have you tried sufficient hyper-parameter search?, ie learning rate, batch size, etc

[csccsccsccsc]

1. I train on the BRACSv1 dataset (".../BRACS_RoI/previous_versions/Version1_MedIA").

2. I try the pre-trained model, but I got:
   Test weighted F1 score 0.4220916765247371
   Test accuracy 0.46166134185303515

3. I have tried learning rate 0.1 / 0.001 / 0.0005 / 0.0001 / 0.00001. I only wait for around 10 epochs. In my experiments, the "val weighted F1 score" and the "val accuracy" keeps unchanged (usually around 0.04527802507158154 and 0.16220735785953178).

[csccsccsccsc]

Will this caused by the different graph features? Could you send me your processed graph features?

[csccsccsccsc]

I found that there exist some empty images in the website:
train/5_DCIS/BRACS_1478_DCIS_24.png, train/5_DCIS/BRACS_1478_DCIS_30.png, train/5_DCIS/BRACS_295_DCIS_31.png
train/6_IC/BRACS_280_IC_14.png
val/6_IC/BRACS_1638_IC_13.png

[guillaumejaume]

Thanks for reporting the issue with empty images. We're working on it, and will fix it as soon as possible.

[guillaumejaume]

You can download the preprocessed cell, tissue and hact graphs for the BRACS dataset here:

• https://ibm.box.com/s/6v4sasavltjzi91gmohswz2ek6i8i3lp

Or by downloading this zip file that includes the test cell graphs:

• https://ibm.box.com/shared/static/412lfz992djt8u6bgu13y9cj9qsurwui.zip

[guillaumejaume]

Empty images updated on the FTP server, thanks for reporting the issue.