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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,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

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,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

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,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

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,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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,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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