11 Computer Vision News Computer Vision News Paul attributes their success to the relatable nature of the problem. “It was easy to grasp because it comes directly from clinical practice,” he points out. “To understand our contribution and the problem in the first place, you don’t need to know the technical details of each step, but the bigger picture is very relatable and understandable. That’s why I think it was a bit easier to present it to people so that they understand. Sometimes, when I’m listening to poster presentations, they’re hard to follow because they’re just so technical. We had this high-level problem, and you could communicate it more easily to people.” Looking ahead, Anna says that while these models are expressive and show the desired uncertainty, sometimes they tend to be overconfident in their predictions, which is a key improvement area. “That’s what we’ve noticed, especially with the betterperforming models,” she reveals. “If we could fix that and adjust this confidence a bit, that would be amazing!” While other works highlight uncertainty as just one part of the picture alongside other things, their work focuses on incorporating it into actual clinical practice and prompts others to explore similar ideas. “Usually, if you ask people, they agree uncertainty is important, but what do we actually do with it?” Paul asks. “This is what we showed in this paper. Maybe other people will come up with nice ideas, and I hope this contributes to that.” Look out for more papers from the team at MICCAI in October. Anna and Paul are keen to emphasize the collaborative nature of their research, working not only as computer scientists but also closely with the clinicians who initially identified the challenges in cup and disc segmentation. “Without the clinicians, without getting what they’re actually struggling with in practice, we wouldn’t have had this idea,” Paul stresses. “I think it’s very important to mention the clinical side of this as well!” “… the bigger picture is very relatable and understandable!” Leveraging Probabilistic Segmentation …
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