Mohamed Medhat Gaber
@mmmgaber
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World’s top 1% in AI & Image Processing (Elsevier/Stanford) | World's top 1% overall & top 1% in Engineering & Computer Science (ScholarGPS)
Birmingham, England
Joined February 2011
Stay on top of the latest AI breakthroughs with the AI Research Radar Podcast! Each episode delivers cutting-edge research and developments in AI simplified for easy understanding. Subscribe to the Times of AI today for immediate access to new episodes. https://t.co/NdWZr7R5ra
youtube.com
The channel provides comprehensive coverage of the latest news and advancements in the field of Artificial Intelligence, carefully curated by Professor Mohamed Medhat Gaber. With a wealth of knowle...
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iPac: Incorporating Intra-image Patch Context Into Graph Neural Networks for Medical Image Classification #artificial_intelligence
#medicalimaging
https://t.co/yT2IROhBlx
link.springer.com
Graph neural networks have emerged as a promising paradigm for image processing, yet their performance in image classification tasks is hindered by a limited consideration of the underlying structure...
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Transform your understanding of neural networks. Master the art and science of designing AI systems that push the boundaries of what's possible. #artificial_intelligence
https://t.co/nVully9BDo
amazon.co.uk
What Makes This Book Essential Journey Through Neural Architecture Key Features Who This Book Is For
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Delve into the science, ethics, and implications of creating minds beyond human design—and discover why artificial general intelligence represents both our greatest technological challenge and perhaps our most profound mirror for understanding ourselves. https://t.co/nwwU1jyJvl
amazon.co.uk
Artificial General Intelligence: A Comprehensive Exploration of Humanity's Most Ambitious Creation What happens when machines can think, learn, and solve problems with the same flexibility as humans?...
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#detrac #ai #DeepLearning #medicalimaging Our DeTraC paper has reached 150 citations on @ResearchGate
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CURVETE: Curriculum Learning and Progressive Self-supervised Training for Medical Image Classification #ai #medicalmaging
https://t.co/IJ3PMYPN1H
link.springer.com
Identifying high-quality and easily accessible annotated samples poses a notable challenge in medical image analysis. Transfer learning techniques, leveraging pre-training data, offer a flexible...
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We are honoured to welcome Prof Barbara Plank (@barbara_plank) from @MaiNLPlab, @CisLmu as our keynote speaker at LoResLM 2026. #NLProc #LoResLM @eaclmeeting
@hh_hansi @TharinduDR Alistair Plum @perayson @ruslanmitkov @mmmgaber Fiona Anting @DamithPremasiri Lasitha Uyangodage
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Our research is available on @ResearchGate: https://t.co/qTpkugeaQo
#medicalimaging #ai #NeuralNetworks
researchgate.net
PDF | Graph neural networks have emerged as a promising paradigm for image processing, yet their performance in image classification tasks is hindered... | Find, read and cite all the research you...
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Our #RandomForests review paper that was published over a decade ago is still in the spotlight. #AI #machinelearning
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The Second Workshop on Language Models for Low-Resource Languages @eaclmeeting 2026 📢CFP - https://t.co/WSrsEyZfxg 📅Submit by 6th January 2026 @hh_hansi @TharinduDR Alistair Plum @perayson @ruslanmitkov @mmmgaber Fiona Anting Tan @DamithPremasiri Lasitha Uyangodage #NLProc
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The Second Workshop on Language Models for Low-Resource Languages @eaclmeeting 2026 📢CFP - https://t.co/WSrsEyZfxg 📅Submit by 6th January 2026 @hh_hansi @TharinduDR Alistair Plum @perayson @ruslanmitkov @mmmgaber Fiona Anting Tan @DamithPremasiri Lasitha Uyangodage #NLProc
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The second edition of LoResLM will take place in Rabat, Morocco, alongside @eaclmeeting 2026. Stay tuned for deadlines & keynote speakers. @hh_hansi @TharinduDR Alistair Plum @perayson @ruslanmitkov @mmmgaber Fiona Anting Tan @DamithPremasiri Lasitha Uyangodage #NLProc #LoResLM
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Excellent resource!
Introducing Qwen3-VL Cookbooks! 🧑🍳 A curated collection of notebooks showcasing the power of Qwen3-VL—via both local deployment and API—across diverse multimodal use cases: ✅ Thinking with Images ✅ Computer-Use Agent ✅ Multimodal Coding ✅ Omni Recognition ✅ Advanced
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It is good to see that the paper is still attracting attention, despite being published over a decade ago! #ai #machinelearing
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