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Surgical Triplet Recognition via Diffusion Model

Liu, Daochang, Hu, Axel, Shah, Mubarak, Xu, Chang

arXiv.org Artificial Intelligence

Surgical triplet recognition is an essential building block to enable next-generation context-aware operating rooms. The goal is to identify the combinations of instruments, verbs, and targets presented in surgical video frames. In this paper, we propose DiffTriplet, a new generative framework for surgical triplet recognition employing the diffusion model, which predicts surgical triplets via iterative denoising. To handle the challenge of triplet association, two unique designs are proposed in our diffusion framework, i.e., association learning and association guidance. During training, we optimize the model in the joint space of triplets and individual components to capture the dependencies among them. At inference, we integrate association constraints into each update of the iterative denoising process, which refines the triplet prediction using the information of individual components. Experiments on the CholecT45 and CholecT50 datasets show the superiority of the proposed method in achieving a new state-of-the-art performance for surgical triplet recognition. Our codes will be released.


Data61 using AI and gamification to diagnose mental health patients ZDNet

#artificialintelligence

Data61, the innovation arm of Commonwealth Scientific and Industrial Research Organisation (CSIRO), has used artificial intelligence (AI) and gamification to help psychiatrists and other clinicians accurately diagnose patients with mental health disorders and help improve overall mental health research. Speaking to ZDNet during D61 Live in Sydney on Wednesday, lead author of the research Amir Dezfouli said the idea for the research brings together his two areas of specialty: neuroscience and AI. "We know from neuroscience that most of the mental health disorders affect how we make decisions. One of the easiest ways to assess that is to complete a simple task or – in this case – a simple computer game, which allows us to record a patient's behaviour," he said. "We then use machine learning AI to analyse this complex data set. This gives us an idea about the underlying pathology and gives us a diagnosis of mental health disorders."


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SEE ALSO: Elon Musk gives us first look at the Boring Company's car elevator Answering a Twitter user's question about his "amazing life," Musk said it consists of "great highs, terrible lows and unrelenting stress." The reality is great highs, terrible lows and unrelenting stress. Musk even gave a recipe for dealing with all that stress, although by his own admission it's probably not the best answer: "take the pain and make sure you really care about what you're doing." During the launch event, Musk spoke about potential manufacturing problems, saying that the next six months will likely be "hell."