Face datasets are considered a primary tool for evaluating the efficacy of face recognition methods. Here we show that in many of the commonly used face datasets, face images can be recognized accurately at a rate significantly higher than random even when no face, hair or clothes features appear in the image. The experiments were done by cutting a small background area from each face image, so that each face dataset provided a new image dataset which included only seemingly blank images. Then, an image classification method was used in order to check the classification accuracy. Experimental results show that the classification accuracy ranged between 13.5% (color FERET) to 99% (YaleB). These results indicate that the performance of face recognition methods measured using face image datasets may be biased. Compilable source code used for this experiment is freely available for download via the internet.
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http://dx.doi.org/10.1007/s11263-008-0143-7 | DOI Listing |
Bioinformatics
January 2025
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
Motivation: Ensuring connectivity and preventing fractures in tubular object segmentation are critical for downstream analyses. Despite advancements in deep neural networks (DNNs) that have significantly improved tubular object segmentation, existing methods still face limitations. They often rely heavily on precise annotations, hindering their scalability to large-scale unlabeled image datasets.
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Department of Gastroenterology and Endoscopy, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, 20132 Milan, Italy.
Patients with inflammatory bowel disease (IBD) face an elevated risk of developing colorectal cancer (CRC). Endoscopic surveillance is a cornerstone in CRC prevention, enabling early detection and intervention. However, despite recent advancements, challenges persist.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Clinic of Cranio-Maxillofacial and Oral Surgery, Center of Dental Medicine, University of Zurich, 8032 Zurich, Switzerland.
This case study highlights the use of cinematic rendering (CR) in preoperative planning for the excision of a cyst in the oral and maxillofacial region of a 60-year-old man. The patient presented with a firm, non-tender mass in the right cheek, clinically suspected to be an epidermoid cyst. Conventional imaging, including dental magnetic resonance imaging (MRI) protocols, confirmed the lesion's size, location, and benign nature.
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January 2025
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Hubei, China. Electronic address:
In the face of climate change and the growing global population, there is an urgent need to accelerate the development of high-yielding crop varieties. To this end, vast amounts of genotype-to-phenotype data have been collected, and many machine learning (ML) models have been developed to predict phenotype from a given genotype. However, the requirement for high densities of single-nucleotide polymorphisms (SNPs) and the labor-intensive collection of phenotypic data are hampering the use of these models to advance breeding.
View Article and Find Full Text PDFJ Exp Bot
January 2025
Noble Research Institute, Ardmore, OK 73401, USA.
Translating biological knowledge from Arabidopsis to crop species is important to advance agriculture and secure food production in the face of dwindling fertilizer resources and biotic and abiotic stresses. However, it is often not trivial to identify functional homologs (orthologs) of Arabidopsis genes in crops. Combining sequence and expression data can improve the correct prediction of orthologs.
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