It is 2026. How are we still publishing articles on medical diagnostics data science and using area under the ROC curve as the primary metric of success. ROC-AUC of 0.9 under severe class imbalance (almost always the case in diagnostics) could still mean something like 4/5 predicted diagnoses are wrong (false positives). Precision-Recall curve + mAP or GTFO.
Also, the most interesting result here is that the CNN-based feature encoder significantly outperformed a vision transformer encoder backbone…
steve-atx-7600 2 hours ago [-]
Teaching students how to interpret evidence must be way undervalued still. I went to one of the top CS schools 20 years ago and you could get a degree without even taking a single probability or stats class of any kind.
chrisjj 6 hours ago [-]
> How are we still publishing articles on medical diagnostics data science and using area under the ROC curve as the primary metric of success.
Waste avoidance.
Bullsh*t is more than sufficient to convince an AI-gulled target audience.
Zaraif13 5 hours ago [-]
I don't agree with the flak Chinese labs get. If it's really that easy to distill and compete with frontier models, why aren't other countries anywhere near this AI race?
felixgallo 3 hours ago [-]
Because distillation is a friendly term for industrial espionage, and most other countries are not willing to become international pariahs in the eyes of the west.
ElProlactin 16 hours ago [-]
I much prefer Gemini. It tells me I'm very smart and almost always right.
vrighter 8 hours ago [-]
you hit the nail right on the head!
pixel_popping 13 hours ago [-]
[flagged]
jkkola 11 hours ago [-]
[flagged]
t0bia_s 15 hours ago [-]
I'm wonder when there will be model for predict future. Could be named "Crystal bAill"
mdp2021 14 hours ago [-]
> model [to] predict future
The attempt is already there: "predicting the future" is called "intelligence".
pizzly 14 hours ago [-]
Isn't detecting cancer predicting a possible future? Hopefully one you can change.
In communism, man exploits man. In capitalism, it's the other way around.
fragmede 19 hours ago [-]
Fortunately, we now have AI to come along and launder the responsibility for exploitation through.
reilly3000 19 hours ago [-]
Now we have AI exploiting us. Look at us race to digitize everything and feed it into ever rising oceans of context, to build infrastructure for its rapid expansion, confident that we can’t stop because we can’t all stop; we’re just humans. All we have to do is stop. And we can’t. That will end up not in our extinction, but in our ever increasing subjugation, both of freedoms and spirit.
hgoel 19 hours ago [-]
Nope, still just humans exploiting humans.
Rendered at 21:06:00 GMT+0000 (UTC) with Wasmer Edge.
Science article in question: https://www.science.org/doi/abs/10.1126/science.aec6129
Also, the most interesting result here is that the CNN-based feature encoder significantly outperformed a vision transformer encoder backbone…
Waste avoidance.
Bullsh*t is more than sufficient to convince an AI-gulled target audience.
The attempt is already there: "predicting the future" is called "intelligence".
> EPS3.9 also had significant anti-tumor effects in the mice with liver cancer and activated anti-tumor immune responses
“A Novel Exopolysaccharide, Highly Prevalent in Marine Spongiibacter, Triggers Pyroptosis to Exhibit Potent Anticancer Effects” (2025) DOI: 10.1096/fj.202500412R https://faseb.onlinelibrary.wiley.com/doi/10.1096/fj.2025004...
"A Gemma model helped discover a new potential cancer therapy pathway" https://news.ycombinator.com/item?id=45604231 :
> eCPMV VNPs + EPS3.9 + [...]
"Scientists are discovering a powerful new way to prevent cancer" https://news.ycombinator.com/item?id=45474404
Notes re: Kidneys not Livers: https://news.ycombinator.com/item?id=47460486 ; gh/topic/healthcare-ai
102M - modified BERT-base-Chinese text encoder
26M - 3D U-Net-style vision/anatomy encoder
2.8M - projection layers, anatomy-specific projections, query tokens and attention layer
Written to run on something like an H100 though- as the CT Scan data is quite large.
Source: https://github.com/alibaba-damo-academy/damo-radar
Model: https://huggingface.co/radar-generalist