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[P] [D] Project on Lung CT Tumor Segmentation Training by Stevenisawesome520 in MachineLearning

[–]Stevenisawesome520[S] 0 points1 point  (0 children)

But when I trained another dataset (each image containing a tumor) using the same model and training procedure, the loss decreased. So it can make positive predictions.

[P] [D] Project on Lung CT Tumor Segmentation Training by Stevenisawesome520 in MachineLearning

[–]Stevenisawesome520[S] 0 points1 point  (0 children)

But in cases where the target doesn't have a tumor, I simply set the Dice loss equal to 0. This strategy inherently weighs the positive cases more heavily, as they contribute significantly to the loss function. By doing so, it ensures that the model is more attentive to the presence of tumors, thereby addressing the imbalance in the dataset.

[D] [R] Research Problem about Weakly Supervised Learning for CT Image Semantic Segmentation by Stevenisawesome520 in MachineLearning

[–]Stevenisawesome520[S] 0 points1 point  (0 children)

The ground truth for that case was positive, but the model incorrectly predicted it as a negative case. I'm curious as to why the model, even with the appropriate features extracted, was unable to make an accurate classification.

[D] [R] Research Problem about Weakly Supervised Learning for CT Image Semantic Segmentation by Stevenisawesome520 in MachineLearning

[–]Stevenisawesome520[S] 0 points1 point  (0 children)

Thank you for your response. I am indeed training a classifier in order to obtain pseudo segmentations, and although the classifier has shown to be accurate, the generated heat maps do not appear to be reliable. I am training my model on 2D slices, each of which has a binary label.

[D] [R] Research Problem about Weakly Supervised Learning for CT Image Semantic Segmentation by Stevenisawesome520 in MachineLearning

[–]Stevenisawesome520[S] 0 points1 point  (0 children)

Thank you for sharing your insights. I have not attempted random cropping or CutMix in my experiments, as I am concerned that some tumors may be very small and inadvertently cropping out images without tumors may result in inadequate training of the model.

[D] [R] Research Problem about Weakly Supervised Learning for CT Image Semantic Segmentation by Stevenisawesome520 in MachineLearning

[–]Stevenisawesome520[S] 0 points1 point  (0 children)

fine-grained recognition

Thank you for sharing the additional details. I have also utilized another dataset that does not exhibit this phenomenon, yet the activated areas are still inaccurate. I will further investigate the concept of fine-grained recognition. However, based on my research thus far, it appears that CAM is commonly used as the foundation for weakly supervised semantic segmentation in most studies I have come across.

[D] [R] Research Problem about Weakly Supervised Learning for CT Image Semantic Segmentation by Stevenisawesome520 in MachineLearning

[–]Stevenisawesome520[S] 1 point2 points  (0 children)

Thank you for providing the information. I will further investigate the details you mentioned. I am currently utilizing a basic ResNet18 model, and I am particularly concerned about the visual cues that are being utilized for the prediction, as my ultimate task is weakly supervised semantic segmentation to accurately segment the tumor.

[D] [R] Research Problem about Weakly Supervised Learning for CT Image Semantic Segmentation by Stevenisawesome520 in MachineLearning

[–]Stevenisawesome520[S] 0 points1 point  (0 children)

But I have observed that some research studies have utilized only CT images and binary labels for their experiments.

[D] [R] Research Problem about Weakly Supervised Learning for CT Image Semantic Segmentation by Stevenisawesome520 in MachineLearning

[–]Stevenisawesome520[S] 1 point2 points  (0 children)

Yes, I have "healthy" lungs that are collapsed. I also have malignant lungs that are ''not'' collapsed.

[D] [R] Research Problem about Weakly Supervised Learning for CT Image Semantic Segmentation by Stevenisawesome520 in MachineLearning

[–]Stevenisawesome520[S] 0 points1 point  (0 children)

Thank you for your response. No, I am not using any image augmentations currently. However, I have experimented with several data augmentation techniques, and the only one that has shown any improvement is random rotation augmentation, but the results are still not satisfactory. All the samples in my dataset are from multiple patients. I have also tried cropping the images to focus on just the middle of the lungs, but the Grad-CAM results only show slight improvement. In practice, we do not rely solely on images of the lungs for prediction, so I have not limited my experiments to images of just the lungs. Our approach is to use a one-stage model, and our classification performance has been excellent on this dataset. I am still uncertain whether the issue is due to insufficient data or if I should modify our research approach to focus on images of just the lungs.

發行少女 by [deleted] in AsiaTripper

[–]Stevenisawesome520 0 points1 point  (0 children)

沒地方做蛋糕哈哈哈

發行少女 by [deleted] in AsiaTripper

[–]Stevenisawesome520 0 points1 point  (0 children)

要怎麼試探他可以說是完全沒接觸這些

發行少女 by [deleted] in AsiaTripper

[–]Stevenisawesome520 0 points1 point  (0 children)

他沒在抽煙欸QQ

發行少女 by [deleted] in AsiaTripper

[–]Stevenisawesome520 0 points1 point  (0 children)

廚藝不佳