224 - Recurrent and Residual U-net

[@filippocastelli42]

Just yesterday I was discussing about what advantages you would have by including residual blocks in a microscopy segmentation UNET and today you cover this exact argument on a video. You're a gift from heaven.

[@70ME3E]

I'm just learning this stuff too for ortophoto segmentation, very timely indeed :)

[@ZhengQing]

谢谢！

[@DigitalSreeni]

Thank you very much for being generous.

[@aleandropresta7552]

I'm preparing my last exam (of my master's degree) that is based on this subjects. I find your videos very helpful, thank you!

[@vivek-eb1ic2309]

its very easily, you taught. very nice!

[@soumyadrip]

Wow, great video. Just wanted to ask if you are thinking about covering the topic of attention based Unet in you up coming videos. It will be great to see that.

[@DigitalSreeni]

Please wait for the next couple of videos…

[@soumyadrip]

@@DigitalSreeni yeah, sure. Will be glad to

[@RohanPaul-AI]

Brilliant explanations. Thanks so much.

[@DigitalSreeni]

Glad it was helpful!

[@jacobusstrydom7017]

Best explanation I have seen. Can't wait for the next video

[@ajay0909]

Wow how did i miss this video, this is very very good content. Thank you so much

[@DigitalSreeni]

You're very welcome!

[@angelceballos8714]

Thanks for being a great teacher!

[@DigitalSreeni]

My pleasure!

[@rojanbasnet1028]

Big fan of your work sir. Thank you for uploading.

[@DigitalSreeni]

Welcome!

[@lalitsingh5150]

Thank you sir for this video..

[@nandeeshnandy5357]

Dear expert i was very comfortable with your explanation my question is it is same for even lung CT images

[@reemawangkheirakpam8165]

Sir can you please make a video on the challenges in instance segmentation

[@aidataverse]

Thanks a lot

[@shuangxinyang6589]

well explained

[@tilkesh]

Thank you.

[@sabrinadhalla]

Thank you sir for the best explanation on this topic....Subscribed :)

[@brianmoon9777]

Thanks for your great video, your video is really helpful. I have a quick question about U-Net, Does it really different between using/replacing residual block in the encoder part when U-net model builds and using ResNet as backbone for U-net? As far as I understand, those two models are very similar in terms of using residual block in encoder part, I know the structure of layers is different, but I am still confused by how different they are. Thanks,

[@sanjeetpatil1249]

In residual Unet architecture, the dropout function seems to misbehave with the architecture. When I tried loading the weights of model after training with dropout , it displayed an error message of shape mismatch. Can you kindly explain the reason behind this?

[@4MyStudents]

if we are doing R(x) = f(x) - x and after that we are adding x ... then how it is making a difference ... we are just passing f(x)

[@salmahayani2710]

HEllo sir do u have any idea how we can use the GRU instead of a simple RNN in the 3D Unet architecture? which means I want to use gru for 3D data without flatten them

[@talha_anwar]

Recurrent reminds me RNN

[@Ajaysharma-yv7zp]

Thanks sir great content... Kindly explain Attention modules in s egmentation and classification

[@DigitalSreeni]

The next couple of videos will be on using attention in Unets.

[@Ajaysharma-yv7zp]

@@DigitalSreeni thanks sir i am a researcher and i have few doubts can i get email

[@XX-vu5jo]

Still waiting for your attention-guided U-Net

[@DigitalSreeni]

Please wait for the next couple of videos…

[@XX-vu5jo]

@@DigitalSreeni yes we are thank you