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| http://dx.doi.org/10.1136/bmj.2.4416.256 | DOI Listing |
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Microvascular reconstruction in oral and maxillofacial surgery: a Europe-wide data collection.
Oral Maxillofac Surg
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Department of Oral and Maxillofacial Surgery, German Armed Forces Central Hospital, Rübenacherstr. 170, 56072, Koblenz, Germany.
Purpose: This study aims to analyze microvascular reconstruction in Oral and Maxillofacial Surgery (OMFS) in Europe.
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Aggregate global features into separable hierarchical lane detection transformer.
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School of Information Engineering, Henan University of Science and Technology, Luoyang, 471000, China.
Lane detection is one of the key functions to ensure the safe driving of autonomous vehicles, and it is a challenging task. In real driving scenarios, external factors inevitably interfere with the lane detection system, such as missing lane markings, harsh weather conditions, and vehicle occlusion. To enhance the accuracy and detection speed of lane detection in complex road environments, this paper proposes an end-to-end lane detection model with a pure Transformer architecture, which exhibits excellent detection performance in complex road scenes.
View Article and Find Full Text PDFWhen multiple instance learning meets foundation models: Advancing histological whole slide image analysis.
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Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Southern Medical University, Guangzhou, China. Electronic address:
Deep multiple instance learning (MIL) pipelines are the mainstream weakly supervised learning methodologies for whole slide image (WSI) classification. However, it remains unclear how these widely used approaches compare to each other, given the recent proliferation of foundation models (FMs) for patch-level embedding and the diversity of slide-level aggregations. This paper implemented and systematically compared six FMs and six recent MIL methods by organizing different feature extractions and aggregations across seven clinically relevant end-to-end prediction tasks using WSIs from 4044 patients with four different cancer types.
View Article and Find Full Text PDFComparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy.
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Department of Biological Sciences, Purdue University, West Lafayette, 47907, Indiana, USA.
RNA plays a crucial role not only in information transfer as messenger RNA during gene expression but also in various biological functions as non-coding RNAs. Understanding mechanical mechanisms of function needs tertiary structure information; however, experimental determination of three-dimensional RNA structures is costly and time-consuming, leading to a substantial gap between RNA sequence and structural data. To address this challenge, we developed NuFold, a novel computational approach that leverages state-of-the-art deep learning architecture to accurately predict RNA tertiary structures.
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