Zongwei Zhou

Hi @jcarta and @Swathi-Guptha If you want to have a positive loss value, you can simply add a constant number 1. to the loss. That is ``` def bce_dice_loss(y_true, y_pred):...

Hi @DBAFC Which implementation did you refer to? I think I used 1x1 convolutions in the output layer as well. See https://github.com/MrGiovanni/UNetPlusPlus/blob/master/keras/helper_functions.py#L135 unet_output = Conv2D(num_class, (1, 1), activation='sigmoid', name='output', kernel_initializer...

Hi, Please find the shared code for both Mask RCNN and Mask RCNN PlusPlus https://drive.google.com/drive/folders/1TQTiCH6WJmP2c28RUTqOgPww3nEJza21?usp=sharing I adopted the basic Mask RCNN implementation from https://github.com/matterport/Mask_RCNN and all the modifications are made...

Hi @Einshowstank The deep supervision code is as below (you don't need to average outputs before computing loss): ``` % Architecture definition def UNetPlusPlus(img_rows, img_cols, color_type=1, num_class=1, connection='concatenation', deep_supervision=False): ......

Hi Fabian, Thank you for your comments, and we greatly admire your work on nnU-Net. Also, thank you for patiently answering the many questions from us (Shivam). With the widespread...

Hi Fabian, The reproduced results that we have shared with you (62.61% +- 0.51%) are evaluated from 5-fold CV, which should correspond to "0.6372" in Table F.6 of https://arxiv.org/pdf/1904.08128.pdf. Unfortunately,...

Hi @squaresoft2015 ] You can try to download the dataset with this link: https://drive.google.com/file/d/13N8VzZaiT-5JsE4bp9ehRRsAgxrDT4xU/view?usp=sharing Best, Zongwei

Hi @squaresoft2015 Sorry, I don't have its Baidu Drive copy because Baidu has a limitation of file size. Thanks, Zongwei

Hi @camilleruppli Thanks for reaching out. Genesis Chest CT was pre-trained by sub-volumes of 64x64x32, and we recommend to keep the same input shape in target tasks as well (we...

Hi @camilleruppli I would say sigmoid and softmax have no major difference if you use them correctly. Usually, for binary, multi-label classification/segmentation, I would use the sigmoid function in the...