Resize transform produces aliasing artifacts

Describe the bug The Resize transform produces aliasing artifacts. It uses the F.interpolate function from PyTorch, which has an antialiasing option, but that does not support 3D downsampling of volumes (5D tensors). The Resize transforms does not use the antialiasing option at all: https://github.com/Project-MONAI/MONAI/blob/a676e3876903e5799eb3d24245872ea4f3a5b152/monai/transforms/spatial/array.py#L676

This line in PyTorch source code confirms that the antialiasing option is not available for 3D volumes (trilinear interpolation): https://github.com/pytorch/pytorch/blob/8ed6cb42ba2635319b67417b9ee2f1a0142d2c25/torch/nn/functional.py#L3851

The issue of ignoring aliasing in deep learning research is covered in this paper: https://arxiv.org/abs/2104.11222

To Reproduce Steps to reproduce the behavior:

1. Resize an image or volume down to a small size, e.g. using a factor of 8
2. Observe the aliasing artifacts in the resized tensor

Expected behavior The downsampled image should look like a smaller version of the original (no aliasing).

[lemairecarl]

Here is an example:

As you can see, the F.interpolate looks similar to Nearest. We can see lots of jagged edges. The bottom of the image is pretty crappy in F.interpolate. The structure is very “fragmented”, and we can observe the same phenomenon elsewhere in the image. On the other hand, in the filtered version (“PIL Bilinear”), each output pixel summarizes a small region in the input image. It’s more faithful.

[lemairecarl]

Hi, thanks for taking this seriously.

I see that you added this to the “label downsampling with antialiasing” milestone. Maybe you should rename the milestone as just “downsampling with antialiasing” ?