mars huang

PhD student at Stanford’s Center for Artificial Intelligence in Medicine & Imaging (AIMI)

[mars huang @MarsScHuang]

Excited to present my latest work, GLoRIA, at #ICCV2021: A Multimodal Global and Local Representation Learning Framework for Label-Efficient Medical Image Recognition.

w/ Liyue Shen, @mattlungrenMD & @syeung10

[Code]: https://t.co/yfRPRWLTlA
[Paper]: https://t.co/mCupfpsmsL

[Mojtaba Seyedhosseini @mseyed]

Gemini Embedding 2: A Native Multimodal Embedding Model from Gemini 🚀

Today, we’re sharing the @GoogleDeepMind white paper for GE 2, our first native multimodal embedding model. Whether it’s text, audio, video, or image, GE 2 provides a unified representation of the input.

[Curt Langlotz @curtlanglotz]

All the CheXpert Plus links in one place:
--Dataset: https://t.co/9ZkvmBTmbg
--Arxiv paper: https://t.co/ggwx6QOS74
--De-ID algorithm paper: https://t.co/DeQvoo9tsw
--Hugging Face De-ID algorithm: https://t.co/28n6hq2X6s

@StanfordAIMI

[Curt Langlotz @curtlanglotz]

CC @stanfordAIMI @stanfordHAI @VinBrainAI @PierreChambon6 @MarsScHuang @zhjohnchan @mayavarma23 @steventruongq @tsounack @IAMJBDEL Chu The Chuong

[Jason Alan Fries @jasonafries]

We're excited to introduce #INSPECT a large-scale ✨3D multimodal✨medical imaging dataset #NeurIPS2023

19,402 Stanford Medicine Patients
🩻23,248 CT Scans + 📄Paired Radiology Notes
📈Longitudinal EHRs
🩺Clinician-validated task labels
#DataCentricAI #Multimodal #3Dimaging

1/

[Jason Alan Fries @jasonafries]

Lots of hype around #LLMs in healthcare. What do clinicians really want from an #LLM? We asked them! Introducing #MedAlign, the first dataset of clinician-generated instructions + responses for EHRs 🏥🤖

📄Paper: https://t.co/Wp1z5AvWll
🌐Website: https://t.co/IvepkoIoLZ

[Nigam Shah @drnigam]

It was fun to summarize lessons learned from research in partnership with @StanfordHAI, @StanfordCRFM, @StanfordMed for our clinical colleagues. We have to verify the claimed value propositions (https://t.co/BIaqfmIQ7L) because they don't always pan out (https://t.co/3UqXecj1Xk)!

[Naoto Usuyama @naotous]

GPT-4 for matching more clinical trials with patients🧐

Featured by @_akhaliq on HuggingFace Daily Papers
https://t.co/0kttBRg9UE

[Michael Wornow @MichaelWornow]

There's a lot of excitement around large language models (LLMs) for healthcare. But what's hype and what's real?

In this paper, we review 84 such models to help health systems better understand and critically evaluate these technologies.

Paper: https://t.co/BzHxYiNmep (1/7)

[NEJM AI @NEJM_AI]

A new systematic review evaluates the impact of self-supervised learning in medical image classification. Findings show improved model performance, especially in radiology. Combining different SSL strategies appears promising. https://t.co/aS0cqyy9mG

[Zhenzhen Weng @JenWeng4]

Pls stop at #CVPR2023 poster *Tue AM 110* to learn about GC-KPL: a novel method for learning 3D human keypoints from point clouds w/o human labels.
Project: https://t.co/XYgT2MtAEW
Joint work w/ awesome folks @gorban Jingwei Ji, @MahyarNajibi, Yin Zhou, Dragomir Anguelov, @Waymo

[Orr Zohar @orr_zohar]

Thanks for tweeting, @AK!  We’re super excited about the future of text-only vision model selection! 🙏

@MarsScHuang
@kcjacksonwang
@syeung10

[Jackson Wang @kcjacksonwang]

Have videos of your tennis practice and wish you can put your own motion in 3D? 🎾 👟 🏋🏻

#CVPR2023 We present, NeMo, a 3D motion recovery method that is more accurate by leveraging information shared across multiple instances/repetitions!

👇🏻Resources in 🧵

[Matthew Lungren MD MPH @mattlungrenMD]

Could Self-Supervised Learning Be a Game-Changer for Medical Image Classification?

“self-supervised training may be a step on the path to a true foundation model for medical image classification” @MarsScHuang

@Dr_ASChaudhari @syeung10 @anujpareek
https://t.co/rh8KV3MZvH

[Serena Yeung-Levy @yeung_levy]

Interested to find & fix error modes of image classifiers using text prompts? Our work to appear at ICLR23 shows when/how we can do this using text embeddings as proxy for image embeddings. Led by @Zhang_Yu_hui w/ @jhaochenz @MarsScHuang @kcjacksonwang @james_y_zou Code released!

[Akshay Chaudhari @Dr_ASChaudhari]

Our new @npjDigitalMed systematic review covers SSL for medical imaging classification. We explore the methods, their benefits, and some future directions here!

Great work co-led by @MarsScHuang + @anujpareek, and great to collaborate with Malte, @mattlungrenMD and @syeung10

[Matthew Lungren MD MPH @mattlungrenMD]

New systematic review and guidelines on self-supervised learning for medical imaging just dropped!

https://t.co/rh8KV3MZvH

Congrats to @MarsScHuang @Dr_ASChaudhari @syeung10 @anujpareek

[npj Digital Medicine @npjDigitalMed]

This #systematicreview of deep learning models that leverage self-supervised learning for medical image classification tasks aggregates the collective knowledge of prior work, consolidates terminology, and offers implementation guidelines.

https://t.co/oIGGp3Vlw9

[Yuhui Zhang @Zhang_Yu_hui]

3. Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image Segmentation

Session: Mon 11/28 4:40PM-5:25PM @ ML4H (Intercontinental New Orleans)

Joint work with @MarsScHuang, @ZhengpingZhou, @mattlungrenMD, @syeung10

[Yuhui Zhang @Zhang_Yu_hui]

2. DrML: Diagnosing and Rectifying Vision Models using Language

Session: Sat 12/3 1:00PM-2:30PM @ Room 388-390

Joint work with @jhaochenz, @MarsScHuang, @kcjacksonwang, @james_y_zou, @syeung10

[Akshay Chaudhari @Dr_ASChaudhari]

Great review led by @MarsScHuang on lessons learned for medical imaging ML during COVID!