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The enactment of cancer via meaningful action rather than recognizing static depiction puts the structures of image consciousness into the wider context along with memory, free imagination and amodal completion, among others. This article thematizes the specific process of cancer detection in radiology, which presupposes a delicate synthesis of the specifics of oncoradiology images and the skilful actions performed by the radiologist. Using this we have implemented the current state-of-the-art tasks and evaluated them on a challenging X-ray dataset.KeywordsAnomaly localisationSelf-supervised learning By isolating the synthetic, self-supervised task from the rest of the training process we perform a more faithful comparison of the tasks, whilst also making the workflow for evaluating over a given dataset quick and easy. To assist with this we have developed nnOOD, a framework that adapts nnU-Net to allow for comparison of self-supervised anomaly localisation methods.
It is also difficult to assess whether a task generalises well for universal anomaly detection, as they are often only tested on a limited range of anomalies. However, it is difficult to compare these methods as it is not clear whether gains in performance are from the task itself or the training pipeline around it. Recently a number of self-supervised methods have been developed that train end-to-end models on healthy data augmented with synthetic anomalies. The wide variety of in-distribution and out-of-distribution data in medical imaging makes universal anomaly detection a challenging task. This review and its practical tips aim to inspire further development in this arena, production of high-yield educational products, use of engaging delivery methods and programs that are tailored to individual learning needs. Overall, the digital space evolving is well placed to cater to the evolving educational needs of oncology learners. It also reviews data behind the following practical tips of 1) strategically combining text with graphics to decrease cognitive load, 2) engaging users through use of interactive elements in digital content, and 3) maximizing impact through thoughtful organization of animations/images. It will summarize best-practice in developing tailored, made-for-screen videos, gamification, and infographics. This article will review the application of the screen-based DL tools that are at educators’ disposal. However, there remains many reservations in the oncological community to adopting and developing DL, largely due to a poor familiarity with the pedagogical evidence base. The evidence for usage of these techniques in medical education has expanded rapidly in recent years. Digital learning (DL) is well-placed to cater to these needs, as it provides teaching options that can be delivered flexibly and on-demand from anywhere in the world. In addition, cancer professionals are notoriously time-poor, meaning there is a need for high quality, accessible and tailored oncological education programs. The field of radiation oncology is rapidly advancing through technological and biomedical innovation backed by robust research evidence. Using this we have implemented the current state-of-the-art tasks and evaluated them on a challenging X-ray dataset.
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