Titlebook: Medical Image Learning with Limited and Noisy Data; Second International Zhiyun Xue,Sameer Antani,Zhaohui Liang Conference proceedings 2023

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ScribSD: Scribble-Supervised Fetal MRI Segmentation Based on Simultaneous Feature and Prediction Sel. However, obtaining a large amount of high-quality manually annotated fetal MRI is time-consuming and requires specialized knowledge, which hinders the widespread application that relies on such data to train a model with good segmentation performance. Using weak annotations such as scribbles can s

defibrillator

Label-Efficient Contrastive Learning-Based Model for Nuclei Detection and Classification in 3D Cardiing-based methods requires a large amount of pixel-wise annotated data, which is time-consuming and labor-intensive, especially in 3D images. An alternative approach is to adapt weak-annotation methods, such as labeling each nucleus with a point, but this method does not extend from 2D histopatholog

江湖骗子

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Dual-Domain Iterative Network with Adaptive Data Consistency for Joint Denoising and Few-Angle Recons. Reducing the dose of the injected tracer is essential for lowering the patient’s radiation exposure, but it will lead to increased image noise. Additionally, the latest dedicated cardiac SPECT scanners typically acquire projections in fewer angles using fewer detectors to reduce hardware expenses

旧式步枪

octogenarian

malign

花争吵

Decoupled Conditional Contrastive Learning with Variable Metadata for Prostate Lesion Detectiondetection. The Prostate Imaging Reporting and Data System (PI-RADS) has standardized interpretation of prostate MRI by defining a score for lesion malignancy. PI-RADS data is readily available from radiology reports but is subject to high inter-reports variability. We propose a new contrastive loss

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FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentatiol annotation is time-consuming and requires specialized expertise. Semi-supervised segmentation methods that leverage both labeled and unlabeled data have shown promise, with contrastive learning emerging as a particularly effective approach. In this paper, we propose a contrastive learning strategy