Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results von An, Chansik, Park, Yae Won, Ahn, Sung Soo, Han, Kyunghwa, Kim, Hwiyoung, Lee, Seung-Koo

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A novel semi-supervised framework for UAV based crop/weed classification von Khan, Shahbaz, Tufail, Muhammad, Khan, Muhammad Tahir, Khan, Zubair Ahmad, Iqbal, Javaid, Alam, Mansoor

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Exploration of physiological sensors, features, and machine learning models for pain intensity estimation von Pouromran, Fatemeh, Radhakrishnan, Srinivasan, Kamarthi, Sagar

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Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases von Maior, Caio B S, Santana, João M M, Lins, Isis D, Moura, Márcio J C

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Identifying disease-gene associations using a convolutional neural network-based model by embedding a biological knowledge graph with entity descriptions von Choi, Wonjun, Lee, Hyunju

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Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens von Khan, Asim, Nawaz, Umair, Ulhaq, Anwaar, Robinson, Randall W

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Predictors of tooth loss: A machine learning approach von Elani, Hawazin W, Batista, André F. M, Thomson, W. Murray, Kawachi, Ichiro, Chiavegatto Filho, Alexandre D. P

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Compare the performance of the models in art classification von Zhao, Wentao, Zhou, Dalin, Qiu, Xinguo, Jiang, Wei

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A fused-image-based approach to detect obstructive sleep apnea using a single-lead ECG and a 2D convolutional neural network von Niroshana, S M Isuru, Zhu, Xin, Nakamura, Keijiro, Chen, Wenxi

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Development and validation of artificial intelligence to detect and diagnose liver lesions from ultrasound images von Tiyarattanachai, Thodsawit, Apiparakoon, Terapap, Marukatat, Sanparith, Sukcharoen, Sasima, Geratikornsupuk, Nopavut, Anukulkarnkusol, Nopporn, Mekaroonkamol, Parit, Tanpowpong, Natthaporn, Sarakul, Pamornmas, Rerknimitr, Rungsun, Chaiteerakij, Roongruedee

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Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning von Shuldiner, Scott R, Boland, Michael V, Ramulu, Pradeep Y, De Moraes, C Gustavo, Elze, Tobias, Myers, Jonathan, Pasquale, Louis, Wellik, Sarah, Yohannan, Jithin

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A natural language processing and deep learning approach to identify child abuse from pediatric electronic medical records von Annapragada, Akshaya V, Donaruma-Kwoh, Marcella M, Annapragada, Ananth V, Starosolski, Zbigniew A

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Implementation of artificial intelligence algorithms for melanoma screening in a primary care setting von Giavina-Bianchi, Mara, de Sousa, Raquel Machado, Paciello, Vitor Zago de Almeida, Vitor, William Gois, Okita, Aline Lissa, Prôa, Renata, Severino, Gian Lucca Dos Santos, Schinaid, Anderson Alves, Espírito Santo, Rafael, Machado, Birajara Soares

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An improved deep learning model for hierarchical classification of protein families von Sandaruwan, Pahalage Dhanushka, Wannige, Champi Thusangi

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Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response von Venezian Povoa, Lucas, Ribeiro, Carlos Henrique Costa, Silva, Israel Tojal da

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Automatic cell counting from stimulated Raman imaging using deep learning von Zhang, Qianqian, Yun, Kyung Keun, Wang, Hao, Yoon, Sang Won, Lu, Fake, Won, Daehan

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Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance von Liu, Shuang, Yu, Haiye, Sui, Yuanyuan, Zhou, Haigen, Zhang, Junhe, Kong, Lijuan, Dang, Jingmin, Zhang, Lei

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Relation classification via BERT with piecewise convolution and focal loss von Liu, Jianyi, Duan, Xi, Zhang, Ru, Sun, Youqiang, Guan, Lei, Lin, Bingjie

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Deep learning for classification of pediatric chest radiographs by WHO’s standardized methodology von Chen, Yiyun, Roberts, Craig S, Ou, Wanmei, Petigara, Tanaz, Goldmacher, Gregory V, Fancourt, Nicholas, Knoll, Maria Deloria

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A stereo matching algorithm based on the improved PSMNet von Huang, Zedong, Gu, Jinan, Li, Jing, Yu, Xuefei

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