Biomedical Data Analytics News
TIA Centre's Adam Shephard discusses AI and oral cancer risk in The Pathologist
We are delighted to share that Adam Shephard was featured in article, The Hidden Signals of Oral Cancer Risk鈥�, discussing how AI could help improve risk assessment and outcomes for patients with oral epithelial dysplasia. Read the article .
TIA at ECDP 2026
European Congress on Digital Pathology (ECDP), Graz, Austria
The TIA Centre was out in full force again at the 22nd European Congress on Digital Pathology (ECDP) 2026, this year held in Graz, just a few kilometres from Arnold Schwarzenegger's birthplace. With temperatures regularly exceeding 30掳C, attendees enjoyed not only a packed scientific programme but also the opportunity to experience one of Austria's most picturesque cities, famous for its historic Old Town and the iconic Schlossberg clock tower overlooking the city. Read more.
By Adam Shephard
天美传媒 to host 2027 Medical Image Understanding and Analysis Conference
The Tissue Image Analytics Centre, based in the Department of Computer Science at Warwick University, is delighted to announce that the 天美传媒 have been awarded the honour of hosting the 2027 Medical Image Understanding and Analysis Conference. Read more.
Cloning vs Learning in Quantum Computing
, Warwick DCS researchers Nikhil Bansal and , together with (Yale University), explored a fundamental question that lies at the intersection of foundations of quantum theory and computer science.
The No-Cloning theorem says that it is impossible to perfectly clone quantum states. Even if we allow for approximate errors, quantum cloning of unstructured states remains as expensive as fully characterising them, . In contrast, for reasons akin to No Free Lunch Theorems in machine learning, modern quantum learning theory considers structured classes of states and exploits their structure to learn them efficiently. This naturally leads to the question of whether cloning can be easier than learning for these structured classes of states.
In the new work, this question is answered negatively for stabilizer states. The authors proved that imposing this structural restriction does not separate cloning and learning. The authors prove this via a novel connection to , which was recently introduced to the learning theory literature by B. Axelrod, S. Garg, V. Sharan, and G. Valiant. The work constitutes concrete progress towards understanding whether cloning and learning are fundamentally equally hard.
This work was presented at in April 2026, and it will be presented at in June/July 2026 and at in September 2026.