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1. Knee or ROC

   Wendt, Veronica; Yu, Byunggu; Kelly, Caleb; Kim, Junwhan (June 2024, Springer LNCS)

   Self-attention transformers have demonstrated accuracy for image classification with smaller data sets. However, a limitation is that tests to-date are based upon single class image detection with known representation of image populations. For instances where the input image classes may be greater than one and test sets that lack full information on representation of image populations, accuracy calculations must adapt. The Receiver Operating Characteristic (ROC) accuracy thresh-old can address the instances of multi-class input images. However, this approach is unsuitable in instances where image population representation is unknown. We consider calculating accuracy using the knee method to determine threshold values on an ad-hoc basis. Results of ROC curve and knee thresholds for a multi-class data set, created from CIFAR-10 images, are discussed for multi-class image detection. 

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2. Knee osteoarthritis severity level classification using whole knee cartilage damage Index and ANN

   https://doi.org/10.1145/3278576.3278585  

   Du, Yaodong; Shan, Juan; Almajalid, Rania; Zhang, Ming (September 2018, IEEE Conference on Connected Health: Applications, Systems and Engineering Technologies)
3. Knee Osteoarthritis Severity Level Classification Using Whole Knee Cartilage Damage Index and ANN

   Y. D. Du, J. Shan (September 2018, ”, IEEE Conference on Connected Health: Applications, Systems and Engineering Technologies)
4. Using Whole Knee Cartilage Damage Index to Predict Knee Osteoarthritis: A Two-year Longitudinal Study

   https://doi.org/10.1109/bibm.2018.8621530  

   Du, Yaodong; Shan, Juan; Almajalid, Rania; Alon, Tomer; Zhang, Ming (December 2018, IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018))
5. Identification of Knee Cartilage Changing Pattern

   https://doi.org/10.3390/app9173469  

   Almajalid, Rania; Shan, Juan; Du, Yaodong; Zhang, Ming (September 2019, Applied Sciences)

   This paper studied the changing pattern of knee cartilage using 3D knee magnetic resonance (MR) images over a 12-month period. As a pilot study, we focused on the medial tibia compartment of the knee joint. To quantify the thickness of cartilage in this compartment, we utilized two methods: one was measurement through manual segmentation of cartilage on each slice of the 3D MR sequence; the other was measurement through cartilage damage index (CDI), which quantified the thickness on a few informative locations on cartilage. We employed the artificial neural networks (ANNs) to model the changing pattern of cartilage thickness. The input feature space was composed of the thickness information at a cartilage location as well as its neighborhood from baseline year data. The output categories were ‘changed’ and ‘no-change’, based on the thickness difference at the same location between the baseline year and the 12-month follow-up data. Different ANN models were trained by using CDI features and manual segmentation features. Further, for each type of feature, individual models were trained at different subregions of the medial tibia compartment, i.e., the bottom part, the middle part, the upper part, and the whole. Based on the experiment results, we found that CDI features generated better prediction performance than manual segmentation, on both whole medial tibia compartment and any subregion. For CDI, the best performance in term of AUC was obtained using the central CDI locations (AUC = 0.766), while the best performance for manual segmentation was obtained using all slices of the 3D MR sequence (AUC = 0.656). As experiment results showed, the CDI method demonstrated a stronger pattern of cartilage change than the manual segmentation method, which required up to 6-hour manual delineation of all MRI slices. The result should be further validated by extending the experiment to other compartments. 

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