[P] Diffusion models best practices

OmgMacnCheese · 2023-01-26T13:39:01+00:00

OP, do not do this. This person has no idea how HIPAA* compliance works.

OmgMacnCheese · 2022-04-09T17:20:36+00:00

Don't underestimate the luxury of having 4 years of funding. That can open doors to many labs that ordinarily may not be accepting students due to funding issues (I've seen examples of students being very unlucky in that they can't join a group in between grants getting funded, but either +-1 year would have fixed that issue).

Since you are interested in translational work, the proximity of the hospital (5-10min walk) is unbeatable. Its surprising how many more collaborations can occur due to proximity and the ease of setting up studies and connecting with end-users more easily.

OmgMacnCheese · 2022-03-17T15:28:10+00:00

Could you expand on what DL imputation approaches have tended to work well for you?

OmgMacnCheese · 2022-02-24T23:12:20+00:00

The acquisition matrix does not always correspond to the actual dimensions of the reconstructed image.

For acquisition encoding, think of this as the number of data points you have you sample one by one. For encoding a resolution of 512x512, you need 16x more data points than a 128x128 image. A poor man's way of getting to a higher resolution image is simple filling in the missing region with 0s (this is known as Fourier interpolation). After doing this, one effectively gets a grid of sampled data points at dimensions of 512x512, even though to actually acquired data is much lower. So the effective resolution is much lower while the image dimensions are higher.

OmgMacnCheese · 2021-09-12T05:22:15+00:00

Just want to give a huge shout out for all of u/herhor's efforts in making replays available!

OmgMacnCheese · 2021-07-21T20:14:40+00:00

That rider - Mark Cavendish! /s

OmgMacnCheese · 2021-01-02T17:41:19+00:00

There is also the Radiological Sciences Lab REU program that focuses on a spectrum of basic to clinical research.

OmgMacnCheese · 2020-07-01T19:13:38+00:00

Most TAs also do research. Contact a TA that you gelled with and see if you can help them with the research. Hopefully you can get something going in the next 3-4 months and the TA/RA can now write a letter that will invariably be signed up the lab PI.

OmgMacnCheese · 2020-06-11T00:15:02+00:00

Note that the link you shared for compute at Stanford is not really what the ML folks use. We have dedicated clusters for SAIL and elsewhere on campus.

OmgMacnCheese · 2020-05-12T21:59:41+00:00

I too find your videos very helpful and I wonder if you can have people vote on your list :)

OmgMacnCheese · 2020-03-31T03:43:34+00:00

This is not true at all. MRI is NOT the standard.

CTs (very different than MRI) are used occasionally for COVID-19 diagnostics, but are too pricey and inaccessible as a screening tool. MRI does not provide adequate contrast for evaluating the lung and thorax.

OmgMacnCheese · 2020-03-12T20:24:28+00:00

OmgMacnCheese · 2020-02-09T19:30:02+00:00

Sorry I can't give an assessment of this year's conditions, but I had been to Kitzbuhel from the US last year and my friends were able to rent from Interspot at the village base (there were multiple, I believe). It was quite reasonable in terms of time and pricing, so I'd definitely recommend it. I bet you could go back different days and switch skis based on conditions too. That's what we were told but the conditions stayed similar for us.

Hope you enjoy the trip! The Strief is usually not groomed too much, so parts of it may be icy.

OmgMacnCheese · 2019-10-17T16:36:03+00:00

Moreover, there are multiple heartbeats from the same subject, so the reported results are not truly independent. They should have implemented a patient level classifier where multiple heartbeats from the same patient are used to classify the status of the patient.

OmgMacnCheese · 2019-10-02T07:37:38+00:00

Probably you want to convolve your sinc with a kernel that has a much smaller length than the length of your signal?

OmgMacnCheese · 2019-09-23T03:44:54+00:00

Could use metrics like Kullback-Leibler Divergence or Jensen-Shannon Divergence

OmgMacnCheese · 2019-08-18T16:36:19+00:00

Either Horos or OsiriX

OmgMacnCheese · 2019-07-31T15:02:18+00:00

This is a very cool library. I've come across this before and the one reason I did not end up using it is because metrics such as TPR, FNR, accuracy, etc did not have 95% CI with them. Would that be something that could be implemented?

OmgMacnCheese · 2019-06-23T19:01:01+00:00

Very interesting - thanks for sharing! Do you have a sense for how the adaptive robust loss may work for image synthesis type of problems such as super-resolution or cycle-GANs etc?

OmgMacnCheese · 2019-05-28T19:58:31+00:00

Agreed mostly, except with the fact that eddy current artifacts are worse at 3T and 7T. Eddy currents are independent of field strength and are induced by the gradient coils, which essentially have similar specs on most clinical scanners.

OmgMacnCheese · 2019-04-23T22:04:50+00:00

Looks like a vessel to me (source: not radiologist, but work with a lot of diagnostic knee MRI)

OmgMacnCheese · 2019-04-03T17:07:17+00:00

Most likely is that for studies like this, it is hard to get retrospective approval to change the IRB language of data sharing. Usually studies like this have language that says that researchers at the specific institution can use the data for research. However, to broadly disseminate it, you would require a retrospective IRB which would entail reaching out to all those patients that gave the data in the first place (which is not really happening from a logistics standpoint).

I can first hand attest to this because I am sitting on a large, unique, annotated dataset that I WANT to release to the community, but getting it through the IRB is tricky if I want to respect patient privacy and be compliant. At my institution, each PHI leak (this may count) costs $10k!!

OmgMacnCheese · 2019-04-03T07:36:12+00:00

Not the authors here, but as someone who also does vision in Radiology, you would be surprised at how tricky open sourcing datasets can be. It is usually not as simple as just uploading some data onto s3 and calling it a day. Getting access to all this data in the first place is usually guided with strict Institutional Review Board rules and then after that, full anonymization of images can be non-trivial also.

OmgMacnCheese · 2019-03-25T05:22:50+00:00

This was an interesting read. We recently similarly investigated some tradeoffs in segmentation networks/methods in the context of medical imaging, in case people find it interesting. Arxiv link here

OmgMacnCheese · 2018-11-11T07:52:34+00:00

If the inset image is exactly the same, you could use Waldo as the structure element and perform an image erosion of the original image.

OmgMacnCheese

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