Titlebook: Medical Image Understanding and Analysis; 28th Annual Conferen Moi Hoon Yap,Connah Kendrick,Reyer Zwiggelaar Conference proceedings 2024 Th

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H-FCBFormer: Hierarchical Fully Convolutional Branch Transformer for?Occlusal Contact Segmentation wng medically true positive semantic segmentation masks derived from expert annotated articulating paper masks and gold standard masks. The proposed model outperforms other machine learning methods evaluated at detecting medically true positive contacts and performs better than dentists in terms of a

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不規(guī)則的跳動

SA-GCN: Scale Adaptive Graph Convolutional Network for ASD Identificationrk that employs adaptive multi-channel graph convolutional network (AM-GCN) for ASD diagnosis. We introduce mutual learning in two parallel AM-GCNs to integrate the complementary information from different atlases. To alleviate the over-smoothing problem, we add attention-based jumping connections i

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YOLO-TL: A Tiny Object Segmentation Framework for?Low Quality Medical Imagesbundant and densely distributed nature within the frame. Based on these difficult thinking on, a method is used SPD convolution to construct a new multi-feature fusion layer, and combined with the SimAM attention mechanism to improve the current YOLOv8 model. YOLO-TL achieves a Dice score of 0.852 a

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Superresolution of?Real-World Multiscale Bone CT Verified with?Clinical Bone Measuresach with training the models on synthetic data, where low-resolution images are produced by subsampling and blurring high-resolution images, as is the common approach when assessing superresolution architectures for medical images. When evaluating performance, we calculate both clinically relevant b

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AdaptiveSAM: Towards Efficient Tuning of SAM for Surgical Scene Segmentationd explicitly for every image, making this solution increasingly tedious with higher data size. In this work, we propose AdaptiveSAM - an adaptive modification of SAM that can adjust to new datasets quickly and efficiently, while enabling text-prompted segmentation. For finetuning AdaptiveSAM, we pro

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Multimodal Deformable Image Registration for?Long-COVID Analysis Based on?Progressive Alignment and?rates a novel Multi-perspective Loss (MPL) function, enhancing state-of-the-art deep learning methods for monomodal registration by making them adaptable for multimodal tasks. The registration results achieve a Dice coefficient score of 0.913, indicating a substantial improvement over the state-of-t

MUTED

s practitioners and scholars across the health humanities, humanities, arts, social sciences, public health,and medicine. The major focus of the volume is to highlight the role of the health humanities inenriching the social, cultural, and phenomenological experience and understanding of illness, health, and wellbeing..978-3-030-26825-1