DenseNet overfitting on 3D MRI volume classification

I was trying to do pCR classification on the I-SPY 1 dataset(Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials (ISPY1) - The Cancer Imaging Archive (TCIA) Public Access - Cancer Imaging Archive Wiki) (about 200 3D MRI volumes). But the 3D densenet from monai package overfits quickly, since the model output all 0 for validation dataset. Anyone do 3D data classification and encounter similar issues before?

longtail distribution?

It is a binary classification, about150 samples with minority class accounting for 27%, so I tried to add weights at the crossentropy loss and used WeightedRandomSampler, but still no luck.

Any dataset transform mistake? Or Any post-processing mistake?

For data transform, I used

train_transforms = Compose([ScaleIntensity(), AddChannel(), RandRotate90(), EnsureType()])

val_transforms = Compose([ScaleIntensity(), AddChannel(), EnsureType()])

It is a image level classification, so I didn’t use post-processing. Just a 3D DenseNet from monai, and weighted CrossEntropy loss.

Yes, I also tried that demo code. I defined the dataset, transforms, a network, and trained it. On the IXI data, it achieved an accuracy of over 0.85. However, on the breast MRI dataset, the performance is much worse (mostly the negative samples are incorrectly predicted). I am not sure where I went wrong.

Using Data Augmentation: For image classification problems, you can increase the number of samples for minority classes by using data augmentation. Data augmentation includes various transformations on images, such as rotation, scaling, and flipping, which can generate new samples.