Code collaboration opportunity for CT radiology AI projects (KiTS, LiTS..)

Having written hand-crafted networks for years, I have recently been testing the meta-experiments framework: you conduct thousands of AI experiments based on a range hyper-parameter values, do ablations, and choose the best performer. I have a near-mature repo which joins the strengths of ray-tune (generates random AI trials, multi-GPU support), neptune (tracks trial progress,metrics, and charts), and fastai. I am seeking interested individuals for a collab in radiology/cancer AI on 3D datasets (#KiTS, #LiTS challenge). Screenshots below showcase the features:

(ray-tune,NVIDI 3090x2)

Raytune live-feeding experiment progress into neptune via callbacks

Neptune in-depth: charts from a single experiment. Do you spot how the LR (figure on top-left) is flat and later decays. That’s the ReduceLROnPlateau callback!

A callback uploads training / validation results per epoch onto neptune in a color-coded scheme(3-classes 3 colors for KiTS). The image below is not captioned so, from the left images are in threes, a greyscale input image, color-coded prediction, (red=kidney, green=tumour) and color-coded ground truth. You will see a lot of black images, because they are slices without tumour.

This is a highly opinionated quasi-library of tools, designed with a very specific task in mind: medical image segmentation and classification.

P.S I work mostly with neovim highly adapted to fast python coding. Interested individuals may contact to share configs.



Dear Dr. Usman,
I am very interested to work on this project. However, I am a medical student and still a beginner at deep learning, I wrote some codes and have experience with AI in medicine. Also, I can be of benefit to you in the clinical part I am a clinical researcher with 25 publications and an active biostatistician with 2 years of experience with R and machine learning in R and Python.
Best Regards

N.B: My CV is available upon request
This is my email:

1 Like

Thanks for getting in touch. Please stay posted for updates and when I upload the project. It will be free to discuss and contribute at that time. In the meanwhile, I have sent you a DM to discuss options for a collab while the project is in its current stage.

@drusmanbashir this is very cool. I don’t think I have the bandwidth right now to help but may in the future, if that is a possibility.

Absolutely. Doctors, computer Scientists, software engineers, aliens (well maybe later) are all welcome onboard! Stay tuned. There’s more to come :slight_smile:

Update: For those interested, I have uploaded a barebones working version of this library at GitHub - drusmanbashir/fran: 3D segmentation of medical imaging using fastai, Neptune, and Ray-Tune

Have you done any work with generative models?

Hi. Not really. I have considered using them for data augmentation, and I might add them in. The way its setup, the library currently supports UNet flavours you can customise. I have just copy-pasted nnUNet’s model that can be tried. I am getting very high scores for kidney (DC .95) and tumour (DC .86) based on this scheme.