Titlebook: Machine Learning in Medical Imaging; 14th International W Xiaohuan Cao,Xuanang Xu,Xi Ouyang Conference proceedings 2024 The Editor(s) (if a

显示全部楼层 · 发表于 2025-3-23 16:54:33

,Cross-view Contrastive Mutual Learning Across Masked Autoencoders for Mammography Diagnosis,is study, we propose a novel cross-view mutual learning method that leverages a Cross-view Masked Autoencoder (CMAE) and a Dual-View Affinity Matrix (DAM) to extract cross-view features and facilitate malignancy classification in mammography. CMAE aims to extract the underlying features from multi-v

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,Boundary-Constrained Graph Network for Tooth Segmentation on 3D Dental Surfaces, have been proposed for automatic tooth segmentation. However, previous tooth segmentation methods often face challenges in accurately delineating boundaries, leading to a decline in overall segmentation performance. In this paper, we propose a boundary-constrained graph-based neural network that es

显示全部楼层 · 发表于 2025-3-24 04:56:26

,FAST-Net: A Coarse-to-fine Pyramid Network for Face-Skull Transformation,uch as forensic facial reconstruction and craniomaxillofacial (CMF) surgery planning. However, this transformation is a challenging task due to the significant differences between the geometric topologies of the face and skull shapes. In this paper, we propose a novel coarse-to-fine face-skull trans

显示全部楼层 · 发表于 2025-3-24 09:17:19

,Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping,ls available. Applying multiple instance learning-based methods or transformer models is computationally expensive as, for each image, all instances have to be processed simultaneously. The MLP-Mixer is an under-explored alternative model to common vision transformers, especially for large-scale dat

显示全部楼层 · 发表于 2025-3-24 14:26:11

,Consistency Loss for Improved Colonoscopy Landmark Detection with Vision Transformers,om the actual diagnosis, manually processing the snapshots taken during the colonoscopy procedure (for medical record keeping) consumes a large amount of the clinician’s time. This can be automated through post-procedural machine learning based algorithms which classify anatomical landmarks in the c

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显示全部楼层 · 发表于 2025-3-25 00:57:52

,Enhancing Anomaly Detection in Melanoma Diagnosis Through Self-Supervised Training and Lesion Comparements. While considerable research has addressed melanoma diagnosis using convolutional neural networks (CNNs) on individual dermatological images, a deeper exploration of lesion comparison within a patient is warranted for enhanced anomaly detection, which often signifies malignancy. In this stud

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Titlebook: Machine Learning in Medical Imaging; 14th International W Xiaohuan Cao,Xuanang Xu,Xi Ouyang Conference proceedings 2024 The Editor(s) (if a

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