Input Tiles to RNN aren't ranked

[thewayofknowing]

I was cross referencing the publication with the code here and I ran into a bit of inconsistency. From the RNN-based slide integration section -

Given a slide and model f, we can obtain a list of the S most interesting tiles within the slide in terms of positive class probability. The ordered sequence of vector representations e = e1, e2,…, eS is the input to an RNN along with a state vector h.

However, in the code the tiles are fed at random (if shuffled) or otherwise in no particular ranked order of class probabilities
Line 236 of RNN_train.py

    if self.shuffle:
        grid = random.sample(grid,len(grid))
    out = []
    s = min(self.s, len(grid))
    for i in range(s):
        img = slide.read_region(grid[i], self.level, (self.size, self.size)).convert('RGB')

Just a little clarification would be greatly appreciated

[aamster]

Hi @thewayofknowing if I understand correctly the authors expect the input to the RNN to be filtered to the tiles with highest probability of positive class. So the code block is shuffling these top tiles. But I agree, then it is not ordered as quoted in the paper

Also the default for shuffle is False, which maybe means the authors didn't set to True