def __init__(self, att_gate=False, net_size='small', top_k_percent=0.2, n_class=2, mut_ex=False,
dropout=False, drop_rate=.25, mil_ins=False, att_only=False):
super(M_CLAM, self).__init__()
self.att_gate = att_gate
self.net_size = net_size
self.top_k_percent = top_k_percent
self.n_class = n_class
self.mut_ex = mut_ex
self.dropout = dropout
self.drop_rate = drop_rate
self.mil_ins = mil_ins
self.att_only = att_only
self.net_shape_dict = {
"small": [1024, 512, 256],
"big": [1024, 512, 384]
}
self.net_shape = self.net_shape_dict[self.net_size]
if self.att_gate:
self.att_net = G_Att_Net(dim_features=self.net_shape[0],
dim_compress_features=self.net_shape[1],
n_hidden_units=self.net_shape[2],
n_class=self.n_class,
dropout=self.dropout,
dropout_rate=self.drop_rate)
else:
self.att_net = NG_Att_Net(dim_features=self.net_shape[0],
dim_compress_features=self.net_shape[1],
n_hidden_units=self.net_shape[2],
n_class=self.n_class,
dropout=self.dropout,
dropout_rate=self.drop_rate)
self.ins_net = Ins(dim_compress_features=self.net_shape[1],
n_class=self.n_class,
top_k_percent=self.top_k_percent,
mut_ex=self.mut_ex)
self.bag_net = M_Bag(dim_compress_features=self.net_shape[1], n_class=self.n_class)
class M_CLAM(tf.keras.Model):
def __init__(self, att_gate=False, net_size='small', top_k_percent=0.2, n_class=2, mut_ex=False,
dropout=False, drop_rate=.25, mil_ins=False, att_only=False):
super(M_CLAM, self).__init__()
self.att_gate = att_gate
self.net_size = net_size
self.top_k_percent = top_k_percent
self.n_class = n_class
self.mut_ex = mut_ex
self.dropout = dropout
self.drop_rate = drop_rate
self.mil_ins = mil_ins
self.att_only = att_only
self.net_shape_dict = {
"small": [1024, 512, 256],
"big": [1024, 512, 384]
}
self.net_shape = self.net_shape_dict[self.net_size]
if self.att_gate:
self.att_net = G_Att_Net(dim_features=self.net_shape[0],
dim_compress_features=self.net_shape[1],
n_hidden_units=self.net_shape[2],
n_class=self.n_class,
dropout=self.dropout,
dropout_rate=self.drop_rate)
else:
self.att_net = NG_Att_Net(dim_features=self.net_shape[0],
dim_compress_features=self.net_shape[1],
n_hidden_units=self.net_shape[2],
n_class=self.n_class,
dropout=self.dropout,
dropout_rate=self.drop_rate)
self.ins_net = Ins(dim_compress_features=self.net_shape[1],
n_class=self.n_class,
top_k_percent=self.top_k_percent,
mut_ex=self.mut_ex)
self.bag_net = M_Bag(dim_compress_features=self.net_shape[1], n_class=self.n_class)
def networks(self):
a_net = self.att_net
i_net = self.ins_net
b_net = self.bag_net
c_nets = [a_net, i_net, b_net]
return c_nets
def clam_model(self):
att_model = self.att_net.att_model()
ins_classifier = self.ins_net.ins_classifier()
bag_classifier = self.bag_net.bag_classifier()
clam_model = [att_model, ins_classifier, bag_classifier]
return clam_model
def call(self, img_features, slide_label):
"""
Args:
img_features -> original 1024-dimensional instance-level feature vectors
slide_label -> ground-truth slide label, could be 0 or 1 for binary classification
"""
h, A = self.att_net.call(img_features)
att_score = A # output from attention network
A = tf.math.softmax(A) # softmax on attention scores
if self.att_only:
return att_score
if self.mil_ins:
ins_labels, ins_logits_unnorm, ins_logits = self.ins_net.call(slide_label, h, A)
slide_score_unnorm, Y_hat, Y_prob, predict_slide_label, Y_true = self.bag_net.call(slide_label, A, h)
return att_score, A, h, ins_labels, ins_logits_unnorm, ins_logits, slide_score_unnorm, \
Y_prob, Y_hat, Y_true, predict_slide_label