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Open-set recognition generalizes a classification task by classifying test samples as one of the known classes from training or "unknown." As novel cancer drug cocktails with improved treatment are continually discovered, classifying patients by treatments can naturally be formulated in terms of an open-set recognition problem. Drawbacks, due to modeling unknown samples during training, arise from straightforward implementations of prior work in healthcare open-set learning. Accordingly, we reframe the problem methodology and apply a recent Gaussian mixture variational autoencoder model, which achieves state-of-the-art results for image datasets, to breast cancer patient data. Not only do we obtain more accurate and robust classification results (14% average F1 increase compared to recent methods), but we also reexamine open-set recognition in terms of deployability to a clinical setting.
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http://dx.doi.org/10.1016/j.artmed.2022.102451 | DOI Listing |
Animals (Basel)
February 2025
Division of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea.
Effective pig farming relies on precise and adaptable animal identification methods, particularly in dynamic environments where new pigs are regularly added to the herd. However, pig face recognition is challenging due to high individual similarity, lighting variations, and occlusions. These factors hinder accurate identification and monitoring.
View Article and Find Full Text PDFIEEE Trans Pattern Anal Mach Intell
March 2025
Open set recognition (OSR) requires models to classify known samples while detecting unknown samples for real-world applications. Existing studies show impressive progress using unknown samples from auxiliary datasets to regularize OSR models, but they have proved to be sensitive to selecting such known outliers. In this paper, we discuss the aforementioned problem from a new perspective: Can we regularize OSR models without elaborately selecting auxiliary known outliers? We first empirically and theoretically explore the role of foregrounds and backgrounds in open set recognition and disclose that: 1) backgrounds that correlate with foregrounds would mislead the model and cause failures when encounters 'partially' known images; 2) Backgrounds unrelated to foregrounds can serve as auxiliary known outliers and provide regularization via global average pooling.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2024
Interest in emerging diseases is increasing due to recent global outbreaks like COVID-19. Unlike general image classification tasks, medical imaging is a multi-label classification that can have multiple diseases simultaneously. Then, suppose the results of all classes do not exceed the thresholds.
View Article and Find Full Text PDFAm J Gastroenterol
February 2025
Department of Computer Science, University of Copenhagen.
Objective: Endoscopic classification of ulcerative colitis (UC) shows high interobserver variation. Previous research demonstrated that artificial intelligence (AI) can match the accuracy of central reading in scoring still images. We now extend this assessment to longer colon segments and integrate AI into clinical workflows, evaluating its use for real-time, video-based classification of disease severity, and as a support system for physicians.
View Article and Find Full Text PDFBiomimetics (Basel)
January 2025
Department of Electronics and Electrical Engineering, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu 30010, Taiwan.
Convolutional neural networks (CNNs) have been widely and successfully demonstrated for closed set recognition in gait identification, but they still lack robustness in open set recognition for unknown classes. To improve the disadvantage, we proposed a convolutional neural network autoencoder (CNN-AE) architecture for user classification based on plantar pressure gait recognition. The model extracted gait features using pressure-sensitive mats, focusing on foot pressure distribution and foot size during walking.
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