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# -*- coding: utf-8 -*-
# Base cell segmentation dataset, based on torch Dataset implementation
#
# @ Fabian Hörst, [email protected]
# Institute for Artifical Intelligence in Medicine,
# University Medicine Essen
import logging
from typing import Callable
import torch
from torch.utils.data import Dataset
logger = logging.getLogger()
logger.addHandler(logging.NullHandler())
from abc import abstractmethod
class CellDataset(Dataset):
def set_transforms(self, transforms: Callable) -> None:
self.transforms = transforms
@abstractmethod
def load_cell_count(self):
"""Load Cell count from cell_count.csv file. File must be located inside the fold folder
Example file beginning:
Image,Neoplastic,Inflammatory,Connective,Dead,Epithelial
0_0.png,4,2,2,0,0
0_1.png,8,1,1,0,0
0_10.png,17,0,1,0,0
0_100.png,10,0,11,0,0
...
"""
pass
@abstractmethod
def get_sampling_weights_tissue(self, gamma: float = 1) -> torch.Tensor:
"""Get sampling weights calculated by tissue type statistics
For this, a file named "weight_config.yaml" with the content:
tissue:
tissue_1: xxx
tissue_2: xxx (name of tissue: count)
...
Must exists in the dataset main folder (parent path, not inside the folds)
Args:
gamma (float, optional): Gamma scaling factor, between 0 and 1.
1 means total balancing, 0 means original weights. Defaults to 1.
Returns:
torch.Tensor: Weights for each sample
"""
@abstractmethod
def get_sampling_weights_cell(self, gamma: float = 1) -> torch.Tensor:
"""Get sampling weights calculated by cell type statistics
Args:
gamma (float, optional): Gamma scaling factor, between 0 and 1.
1 means total balancing, 0 means original weights. Defaults to 1.
Returns:
torch.Tensor: Weights for each sample
"""
def get_sampling_weights_cell_tissue(self, gamma: float = 1) -> torch.Tensor:
"""Get combined sampling weights by calculating tissue and cell sampling weights,
normalizing them and adding them up to yield one score.
Args:
gamma (float, optional): Gamma scaling factor, between 0 and 1.
1 means total balancing, 0 means original weights. Defaults to 1.
Returns:
torch.Tensor: Weights for each sample
"""
assert 0 <= gamma <= 1, "Gamma must be between 0 and 1"
tw = self.get_sampling_weights_tissue(gamma)
cw = self.get_sampling_weights_cell(gamma)
weights = tw / torch.max(tw) + cw / torch.max(cw)
return weights
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