02.06.2018 · I’m trying to implement a multi-class cross entropy loss function in pytorch, for a 10 class semantic segmentation problem. The shape of the predictions and labels are both [4, 10, 256, 256] where 4 is the batch size, 10 the number of channels, 256x256 the height and width of the images. The following implementation in numpy works, but I’m having difficulty trying to …
22.03.2019 · I’m doing a semantic segmentation problem where each pixel may belong to one or more classes. However, I cannot find a suitable loss function to compute binary crossent loss over each pixel in the image. BCELoss requires a single scalar value as the target, while CrossEntropyLoss allows only one class for each pixel. Is there any built-in loss for this …
21.02.2021 · I am working on multi-class image segmentation and currently having challenges regarding my dataset. The labels (ground truth/target) are already one-hot encoded for the two class labels but the background are not given. Firstly, is the annotation or labeling of the background necessary for the performance of the model since it will be dropped during …
12.09.2020 · Date First Author Title Conference/Journal; 20200821: Nick Byrne: A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI arxiv: STACOM: 20200720: Boris Shirokikh: Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation arxiv: MICCAI 2020
... segmentation losses. Adapted from an awesome repo with pytorch utils ... Loss multiclass mode suppose you are solving multi-class segmentation task.
31.12.2018 · Reduce each loss into a scalar, sum the losses and backpropagate the resulting loss. Side note; make sure your reduction scheme makes sense (e.g. if you are using reduction='sum' and the losses correspond to a multi-label classification, remember that the number of classes per objective is different, so the relative weight contributed by each loss …