Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021; 24th International C Marleen de Bruijne,Philippe C. Cattin,Caroli

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A Hierarchical Feature Constraint to?Camouflage Medical Adversarial Attacksing. Recent findings have shown that existing medical AEs are easy to detect in feature space. To better understand this phenomenon, we thoroughly investigate the characteristic of traditional medical AEs in feature space. Specifically, we first perform a stress test to reveal the vulnerability of m

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Tdd526

UTNet: A Hybrid Transformer Architecture for Medical Image Segmentationmain largely unexplored. In this study, we present UTNet, a simple yet powerful hybrid Transformer architecture that integrates self-attention into a convolutional neural network for enhancing medical image segmentation. UTNet applies self-attention modules in both encoder and decoder for capturing

農(nóng)學(xué)

AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generived growing research interests. Different from the general image captioning tasks, medical report generation is more challenging for data-driven neural models. This is mainly due to 1) the serious data bias: the normal visual regions dominate the dataset over the abnormal visual regions, and 2) the

Trochlea

Continuous-Time Deep Glioma Growth Modelsse distribution in radiation therapy. Recent work has approached the glioma growth modeling problem via deep learning and variational inference, thus learning growth dynamics entirely from a real patient data distribution. So far, this approach was constrained to predefined image acquisition interva

outset

Spine-Transformers: Vertebra Detection and Localization in Arbitrary Field-of-View Spine CT with Trael transformers-based 3D object detection method that views automatic detection of vertebrae in arbitrary FOV CT scans as an one-to-one set prediction problem. The main components of the new framework, called Spine-Transformers, are an one-to-one set based global loss that forces unique predictions

OGLE

Multi-view Analysis of Unregistered Medical Images Using Cross-View Transformersrms of misalignment can make it difficult to combine views effectively, as registration is not always possible. Without registration, views can only be combined at a global feature level, by joining feature vectors after global pooling. We present a novel cross-view transformer method to transfer in

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悲觀

Priapism

Generative Self-training for Cross-Domain Unsupervised Tagged-to-Cine MRI Synthesisp learning model in a source domain to unlabeled target domains. However, while the self-training UDA has demonstrated its effectiveness on discriminative tasks, such as classification and segmentation, via the reliable pseudo-label selection based on the softmax discrete histogram, the self-trainin