def loss(outputs, labels):
f = torch.nn.BCELoss(reduction='none')
floss = f(outputs,labels)
dloss = bratsUtils.bratsDiceLoss(outputs, labels, nonSquared=True)
num_voxels = np.prod(floss.shape)
res1, _ = torch.topk(floss.view((-1, )), int(num_voxels * topK), sorted=False)
num_voxels = np.prod(dloss.shape)
res2, _ = torch.topk(dloss.view((-1, )), int(num_voxels * topK), sorted=False)#for batch_size >1
if len(res2)==0:
return floss.mean()+dloss.mean()+res1.mean()
else:
return floss.mean()+dloss.mean()+res1.mean()+res2.mean()
def loss(outputs, labels):
f = torch.nn.BCELoss(reduction='none')
floss = f(outputs, labels)
filter = torch.zeros(2, 2, 3, 3, 3).cuda()
filter[:, :, 0, 1, 1] = 1
filter[:, :, 1, 1, 0] = 1
filter[:, :, 0, 1, 1] = 1
filter[:, :, 2, 1, 1] = 1
filter[:, :, 1, 1, 2] = 1
filter[:, :, 2, 1, 1] = 1
filter[:, :, 1, 1, 1] = -6
mask = F.conv3d(labels, filter, padding=1)
floss = torch.mean(floss) + 2 * torch.mean(floss[mask != 0])
return bratsUtils.bratsDiceLoss(outputs, labels,
nonSquared=True).mean() + floss
def loss(outputs, labels):
return bratsUtils.bratsDiceLoss(outputs, labels, nonSquared=True)
def loss(outputs, labels):
f1 = torch.nn.BCELoss(reduction='mean')
f2 = torch.nn.MSELoss(reduction='mean')
return bratsUtils.bratsDiceLoss(outputs, labels, nonSquared=True) + f1(
outputs, labels) + f2(outputs, labels)
def loss(outputs, labels):
f = torch.nn.BCELoss(reduction='mean')
return bratsUtils.bratsDiceLoss(
outputs, labels,
nonSquared=True).mean() + 10 * f(outputs, labels)
