Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops; ISIC 2023, Care-AI 2 M. Emre Celebi,Md Sirajus Salekin,

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Multimodal Learning for?Improving Performance and?Explainability of?Chest X-Ray Classificationk-box nature of these models, undermining their explainability. A few of the approaches for “opening” the black box include the use of heatmaps to assist with visual interpretation, but these heatmaps remain crude. While it has been shown that utilizing radiologists’ attention-related data improves

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Cross-Task Attention Network: Improving Multi-task Learning for?Medical Imaging Applicationsperformance. In medical imaging, MTL has shown great potential to solve various tasks. However, existing MTL architectures in medical imaging are limited in sharing information across tasks, reducing the potential performance improvements of MTL. In this study, we introduce a novel attention-based M

pester

Input Augmentation with?SAM: Boosting Medical Image Segmentation with?Segmentation Foundation Model 11 million images with over 1 billion masks and can produce segmentation results for a wide range of objects in natural scene images. SAM can be viewed as a general perception model for segmentation (partitioning images into semantically meaningful regions). Thus, how to utilize such a large founda

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Empirical Analysis of?a?Segmentation Foundation Model in?Prostate Imagingined tasks in a supervised fashion, which requires expensive labeled datasets. Recent advances in several machine learning domains, such as natural language generation have demonstrated the feasibility and utility of building foundation models that can be customized for various downstream tasks with

irreducible

GPT4MIA: Utilizing Generative Pre-trained Transformer (GPT-3) as?a?Plug-and-Play Transductive Model erence tool for medical image analysis (MIA). We provide theoretical analysis on why a large pre-trained language model such as GPT-3 can be used as a plug-and-play transductive inference model for MIA. At the methodological level, we develop several technical treatments to improve the efficiency an

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Carlos Santiago,Miguel Correia,Maria Rita Verdelho,Alceu Bissoto,Catarina Baratae counterparts, the Beilstein Databases of factual and structural data. The enormous work of the staff of the Beilstein Institute has produced, for over 100 years, a very valuable and unique scientific resource. We are pleased to be able to be involved in making this large volume of evaluated scient

penance

Naren Akash R J,Anirudh Kaushik,Jayanthi Sivaswamye counterparts, the Beilstein Databases of factual and structural data. The enormous work of the staff of the Beilstein Institute has produced, for over 100 years, a very valuable and unique scientific resource. We are pleased to be able to be involved in making this large volume of evaluated scient

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oot. Our primary interest in studying the muscles arose from observations of variations, in which a new form of the anomalous muscle in the popliteal fossa had been described (Cihak, 1954; Hnevkovsky and Cihak, 1957) and in which changes of muscle forms in the congenitally malformed extremity had al

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