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"""
Copyright 2021, Dana-Farber Cancer Institute and Weill Cornell Medicine
License: GNU GPL 2.0
"""
import torch
import torch.nn as nn
from torch_geometric.nn.models import MLP
from pathml.ml.layers import GNNLayer
from pathml.ml.utils import scatter_sum
class HACTNet(nn.Module):
"""
Hierarchical cell-to-tissue model for supervised prediction using cell and tissue graphs.
Args:
cell_params (dict): Dictionary containing parameters for cell graph GNN.
tissue_params (dict): Dictionary containing parameters for tissue graph GNN.
classifier_params (dict): Dictionary containing parameters for prediction MLP.
References:
Pati, P., Jaume, G., Foncubierta-Rodriguez, A., Feroce, F., Anniciello, A.M., Scognamiglio, G., Brancati, N., Fiche, M.,
Dubruc, E., Riccio, D. and Di Bonito, M., 2022.
Hierarchical graph representations in digital pathology.
Medical image analysis, 75, p.102264.
"""
def __init__(self, cell_params, tissue_params, classifier_params):
super().__init__()
# Get cell and tissue graph readout operations
self.cell_readout_op = cell_params["readout_op"]
self.tissue_readout_op = tissue_params["readout_op"]
# Modify tissue GNN parameters
if self.cell_readout_op == "concat":
tissue_params["in_channels"] = (
tissue_params["in_channels"]
+ cell_params["out_channels"] * cell_params["num_layers"]
)
else:
tissue_params["in_channels"] = (
tissue_params["in_channels"] + cell_params["out_channels"]
)
# Main GNN model for cell and tissue graphs
self.cell_gnn = GNNLayer(**cell_params)
self.tissue_gnn = GNNLayer(**tissue_params)
# Modify classifier parameters
if self.tissue_readout_op == "concat":
classifier_params["in_channels"] = (
tissue_params["out_channels"] * tissue_params["num_layers"]
)
else:
classifier_params["in_channels"] = tissue_params["out_channels"]
# Main classifier head
self.classifier = MLP(**classifier_params)
def forward(self, batch):
x_cell = batch.x_cell
x_tissue = batch.x_tissue
z_cell = self.cell_gnn(
x_cell, batch.edge_index_cell, batch.x_cell_batch, with_readout=False
)
out = torch.zeros(
(x_tissue.shape[0], z_cell.shape[1]),
dtype=z_cell.dtype,
device=z_cell.device,
)
z_cell_to_tissue = scatter_sum(z_cell, batch.assignment, dim=0, out=out)
x_tissue = torch.cat((z_cell_to_tissue, x_tissue), dim=1)
z_tissue = self.tissue_gnn(
x_tissue, batch.edge_index_tissue, batch.x_tissue_batch
)
out = self.classifier(z_tissue)
return out
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