Is there any reason you're really restraining to a Unet based model ?
I'd recommend testing different architectures such as DeepLab V3 or FPN and see whether stuff improves.
If it doesn't I'd recommend looking to your data and the quality of the ground truth as with only 100 data points you should be very much limited by the information contained in your data.
If the data is clean I'd recommend using some kind of ensemble method, this might be overkill, especially with heavy models but having multiple models with random initializations infer on a same input generally gives a few more points of accuracy/dice so if you really need it , this is an option.
azorsenpai t1_je6hjpu wrote
Reply to [D] Improvements/alternatives to U-net for medical images segmentation? by viertys
Is there any reason you're really restraining to a Unet based model ? I'd recommend testing different architectures such as DeepLab V3 or FPN and see whether stuff improves. If it doesn't I'd recommend looking to your data and the quality of the ground truth as with only 100 data points you should be very much limited by the information contained in your data.
If the data is clean I'd recommend using some kind of ensemble method, this might be overkill, especially with heavy models but having multiple models with random initializations infer on a same input generally gives a few more points of accuracy/dice so if you really need it , this is an option.