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PEDRA-EFB0: colorectal cancer prognostication using deep learning with patch embeddings and dual residual attention.

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Radiol Dept, Jiangnan Univ, Affiliated Hosp, Wuxi, 214122, Jiangsu, People's Republic of China.

In computer-aided diagnosis systems, precise feature extraction from CT scans of colorectal cancer using deep learning is essential for effective prognosis. However, existing convolutional neural networks struggle to capture long-range dependencies and contextual information, resulting in incomplete CT feature extraction. To address this, the PEDRA-EFB0 architecture integrates patch embeddings and a dual residual attention mechanism for enhanced feature extraction and survival prediction in colorectal cancer CT scans.

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Background And Aims: Computer-aided detection software (CADe) has shown promising results in real-time polyp detection, but a limited head-to-head comparison of the available CADe systems has been performed. Moreover, such systems have not been compared to endoscopists using standardized videos. This study aims to compare the performance of three CADe systems in detecting polyps, employing a novel standardized methodology.

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Background: Anatomically formed healing abutments were suggested in literature to address many of the issues associated with immediate posterior implant insertion such as large extraction sockets that are extremely hard to seal without reflecting the mucoperiosteal flap, extraction sockets anatomy that are not suitable for regular healing abutment placement, and potentially high occlusal stresses when planning a temporary implant supported prothesis to improve the conditioning of supra implant tissue architecture and the emergence profile of the implant supported restorations.

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Reducing reading time and assessing disease in capsule endoscopy videos: A deep learning approach.

Int J Med Inform

January 2025

University of Coimbra, Faculty of Medicine, Coimbra, Portugal; Department of Gastroenterology, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal. Electronic address:

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Purpose: This retrospective clinical study aimed to evaluate the clinical outcomes of surveyed monolithic computer-aided design and computer-aided manufacturing (CAD/CAM) zirconia crowns for abutment teeth in removable partial dentures (RPDs).

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