X-Med @X_MedAI X Profile

X-Med

@X_MedAI

Followers

53

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Media

24

Statuses

29

Medical Imaging and Analysis Group @HKUST led by Prof. Xiaomeng Li @xiaomeng_hkust.

https://t.co/jOXkKkfHKF

Joined October 2023

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X-Med

@X_MedAI

10 months

🚨 Our ICLR 2025 poster is live today! MedRegA: an interpretable bilingual generalist for diverse biomedical tasks, with strong regional awareness across 8 modalities 🧠🫁🩻 🌐 https://t.co/AVLxoRpM0e 📍 Poster Session: 🗓️ April 25 | ⏰ 10:00–12:30 SGT 📌 Hall 3 + Hall 2B, #21

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X-Med

@X_MedAI

1 year

#MICCAI2024 ✨Glad to see Thoth and Anubis journey from Egypt 🇪🇬 to Marrakech 🇲🇦! Congratulations to all winners 🎉

Marawan Elbatel

@marwankefah

1 year

🎉 TriALS 2024 has concluded! Honoring the winners with monetary and honorary awards, inspired by Thoth (god of knowledge) and Anubis (protector of the afterlife). 🥇 @mic_dkfz  🥈 @ViCOROB  🥉 @enalmuo Congratulations to all participants and winners! #MICCAI2024

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Marawan Elbatel

@marwankefah

1 year

🌱 Take the Quiz at #MICCAI2024! 🌱 Did you know that an organism starts with a single pix-cell? 🧬✨ 📍 Visit us at poster W-AM-019 from 10:30 to 11:30 am to explore our work on high-res image synthesis. 📝Quiz Link: https://t.co/qX3KYk4YfW @MICCAI_Society @X_MedAI

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X-Med

@X_MedAI

1 year

🚀 Thrilled our lab is represented @MICCAI_Society with 7 main conference papers (including an oral presentation) and a challenge. Huge kudos to our incredible students, clinicians, academic, and industry partners for making this possible! Check it out at #MICCAI2024!

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X-Med

@X_MedAI

1 year

🚨 Oral Presentation at #ECCV2024! 🫀 Discover how CardiacNet reconstructs heart abnormalities for disease assessment from echocardiogram videos. 📍Oral Session 3B | Wednesday, Oct 2 | 9:20–9:30 a.m. 📍Poster #154 | 10:30 a.m.–12:30 p.m. 🔗 Code & data: https://t.co/e229kbdXM6

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X-Med

@X_MedAI

1 year

🧠✨ Unlocking Number Sense with CLIP! Join us at #ECCV2024 🇮🇹 and see how we teach CLIP to master ordinal regression! 📍 Poster #195 🕟Thu, Oct 3 | 4:30–6:30 p.m. CEST GitHub: https://t.co/H3rAKtKcf4 Paper: https://t.co/5gBlnOsxuu

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X-Med

@X_MedAI

2 years

It’s not too late to contribute to #MICCAI2024! Join our TriALS competition to detect liver lesions using non-contrast CT scans in Egyptian patients! Hosted in person at MICCAI. Top teams will present with awards totaling $1,000. 🏆 Participate Now: https://t.co/QKm6oC4Mfa

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X-Med

@X_MedAI

2 years

🌟Interested in domain adaptation for fetal ultrasound? Dive into our insights on the new Fetal Ultrasound Benchmark from two health centers at #ICML2024. 🕑Thu 25 Jul, 2:30-4 p.m. EEST. 📍Hall C 4-9 #207 Paper: https://t.co/SyxkHBLJ5A Code: https://t.co/JSYU0kWtw5

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X-Med

@X_MedAI

2 years

🔴 Live from Arch 4A-E Poster #150 @CVPR: Our PhD student Yiqun Lin presents C²RV - a new method using fewer projection views to reconstruct CT, reducing ionizing radiation exposure for safer interventional radiology. #CVPR2024 💫

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X-Med

@X_MedAI

2 years

Interested in how @CVPR can contribute to healthcare? Stop by X-Med posters at #CVPR2024 today from 10:30 am - 12:30 pm in Arch 4A-E. Check out posters #150 and #191 to learn about the latest applications of computer vision in medical scenarios 🩻. #CVPR2024 #HealthcareTech

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X-Med

@X_MedAI

2 years

🔴 Live from @CVPR: Haonan Wang presenting new SOTAs in Semi-supervised semantic segmentation. Check AllSpark @ Arch 4A-E Poster #335 #CVPR2024 @haonan_hkust

Haonan Wang

@haonan_hkust

2 years

New SOTAs in Semi-supervised Semantic Segmentation with transformers! 🚀🎉 Check out our #CVPR2024 paper, AllSpark, a novel plug-and-play module that unlocks the potential of transformers for SSSS! 💡🔧 1/N Paper: https://t.co/rXtxhmDKcs Code: https://t.co/R6upH5PiBW

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Xiaomeng Li

@xiaomeng_hkust

2 years

I am be attending #CVPR2024 in Seattle. Feel free to stop by our poster session and say hello!

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X-Med

@X_MedAI

2 years

Looking for the SOTAs in Semi-supervised Semantic Segmentation? Check AllSpark to be presented at #CVPR2024 💫🔮 Paper: https://t.co/F1W83y1O2Y Code: https://t.co/XOMEaO1gC2

Haonan Wang

@haonan_hkust

2 years

New SOTAs in Semi-supervised Semantic Segmentation with transformers! 🚀🎉 Check out our #CVPR2024 paper, AllSpark, a novel plug-and-play module that unlocks the potential of transformers for SSSS! 💡🔧 1/N Paper: https://t.co/rXtxhmDKcs Code: https://t.co/R6upH5PiBW

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X-Med

@X_MedAI

2 years

✨Thrilled to announce 4 papers accepted at #CVPR2024 on fetal cardiac detection, sparse CT reconstruction, multi-modal LLMs, & semi-supervised segmentation. Kudos to the authors! Stay tuned for details. 🩻🎉

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X-Med

@X_MedAI

2 years

A comprehensive benchmark with 4 x medical datasets and 12 x baselines for volumetric image segmentation available 👇 #NeurIPS #NeurIPS2023 2/2 https://t.co/1KlUpOQohL

github.com

NeurIPS 2023: Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation - xmed-lab/GenericSSL

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X-Med

@X_MedAI

2 years

Interested in Semi-Supervised Learning or Unsupervised Domain Adaptation for medical imaging at #NeurIPS2023! Check out our poster #224 titled "Towards Generic Semi-Supervised Framework", a step toward Semi-Supervised Domain Generalization. 1/2 Paper: https://t.co/VOx4v8MWKw

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X-Med

@X_MedAI

2 years

One day left until the Digital Pathology and Data Intelligence workshop at HKUST! Excited to welcome speakers including Prof. @james_y_zou, sharing their expertise in medical image analysis. Zoom Link Reg. https://t.co/PFWliWuPt7

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X-Med

@X_MedAI

2 years

#ICCV2023 🇫🇷 ! How to teach the pre-trained CLIP model to detect out-of-distribution (OOD) data? We present CLIPN for Zero-Shot OOD Detection: Teaching CLIP to Say No! Brilliant idea by @silang_whl. Paper: https://t.co/vdj2kyOdio Code: https://t.co/iHArUEnLpd

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X-Med

@X_MedAI

2 years

#ICCV2023 🇫🇷 Oral🚨! Unsupervised domain adaptation (UDA) for echocardiogram segmentation. Introducing CardiacUDA dataset with GraphEcho featuring Graph Matching and Temporal Cycle Consistency modules. 12/n Paper: https://t.co/4SMCKvVl4B Code: https://t.co/dh8mtTgecK

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X-Med

@X_MedAI

2 years

#MICCAI2023 🇨🇦! Inspired by Morphology, a first attempt to explore a deep learning method for unsupervised gland segmentation, where no manual annotations are required. Work done by Qixiang Zhang. 10/n Paper: https://t.co/UNDdvSaU3W Code: https://t.co/ND6v8zIxoG

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