3/19/2023 0 Comments Inmr contact![]() Furthermore, it was also observed that pre-training the models on a different, even unrelated dataset before training them for the task of tumour classification improves the performance. It was observed that both these models performed superior to the pure 3D convolutional model, ResNet18. ![]() This paper uses two spatiotemporal models, ResNet (2+1)D and ResNet Mixed Convolution, to classify different types of brain tumours. These models have the capabilities of learning specific spatial and temporal relationships while reducing computational costs. However, by treating one spatial dimension separately or by considering the slices as a sequence of images over time, spatiotemporal models can be employed as “spatiospatial” models for this task. Typically those methods are either 3D models, which use 3D volumetric MRIs or even 2D models considering each slice separately. Classifying tumours using such deep learning methods has made significant progress with the availability of open datasets with reliable annotations. Deep Learning methods in computer vision applications have shown significant improvement in recent years, most of which can be credited to the fact that a sizeable amount of data is available to train models, and the improvements in the model architectures yield better approximations in a supervised setting. An accurate diagnosis is essential for successful treatment planning, and magnetic resonance imaging is the principal imaging modality for diagnosing brain tumours and their extent. A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and also form metastases.
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