DiceMetric: mean_batch and mean_channel reduction modes appear to be swapped #8602

Describe the bug
The mean_batch and mean_channel reduction modes in DiceMetric.aggregate() appear to be swapped. mean_batch returns one value per channel (instead of per batch), and mean_channel returns one value per batch (instead of per channel).

Is this the intended behavior, or am I misunderstanding how data should be accumulated or passed to DiceMetric? In the below example, the dimension of the data is BCHW.

To Reproduce
Run:

import torch
from monai.metrics import DiceMetric

n_batches = 3
batch_size, n_classes, h, w = 4, 5, 128, 128

y_pred_batches = [torch.rand(batch_size, n_classes, h, w) for _ in range(n_batches)]
y_batches = [torch.rand(batch_size, n_classes, h, w) for _ in range(n_batches)]

dm = DiceMetric()

for i in range(n_batches):
    dm(y_pred_batches[i], y_batches[i])

# Internal buffer has shape [12, 5] (12 samples total, 5 channels)
print(f"Buffer shape: {dm.get_buffer().shape}")  # [12, 5]

# mean_batch returns 5 values (one per channel, not per batch)
print(f"mean_batch: {dm.aggregate(reduction='mean_batch').shape}")  # [5]

# mean_channel returns 12 values (one per batch, not per channel)  
print(f"mean_channel: {dm.aggregate(reduction='mean_channel').shape}")  # [12]

Expected behavior
mean_batch should average across channels and return one value per batch: shape [3]
mean_channel should average across batches and return one value per channel: shape [5]

Environment

Ensuring you use the relevant python executable, please paste the output of:

MONAI version: 1.5.0
Numpy version: 2.2.6
Pytorch version: 2.6.0+cu124

Additional context
The problem appears to be in monai/metrics/utils.py:do_metric_reduction() lines 113-121. I am not sure if the bug affects other metrics using this function too.