Accurate glioma subtype classification is critical for the treatment management of patients with brain tumors. Developing an automatically computer-aided algorithm for glioma subtype classification is challenging due to many factors. One of the difficulties is the label constraint. Specifically, each case is simply labeled the glioma subtype without precise annotations of lesion regions information. In this paper, we propose a novel hybrid fully convolutional neural network (CNN)-based method for glioma subtype classification using both whole slide imaging (WSI) and multiparametric magnetic resonance imagings (mpMRIs). It is comprised of two methods: a WSI-based method and a mpMRIs-based method. For the WSI-based method, we categorize the glioma subtype using a 2D CNN on WSIs. To overcome the label constraint issue, we extract the truly representative patches for the glioma subtype classification in a weakly supervised fashion. For the mpMRIs-based method, we develop a 3D CNN-based method by analyzing the mpMRIs. The mpMRIs-based method consists of brain tumor segmentation and classification. Finally, to enhance the robustness of the predictions, we fuse the WSI-based and mpMRIs-based results guided by a confidence index. The experimental results on the validation dataset in the competition of CPM-RadPath 2020 show the comprehensive judgments from both two modalities can achieve better performance than the ones by solely using WSI or mpMRIs. Furthermore, our result using the proposed method ranks the third place in the CPM-RadPath 2020 in the testing phase. The proposed method demonstrates a competitive performance, which is creditable to the success of weakly supervised approach and the strategy of label agreement from multi-modality data.
Most existing camera placement algorithms focus on coverage and/or visibility analysis, which ensures that the object of interest is visible in the camera's field of view (FOV). However, visibility is inadequate for continuous and automated tracking. In such applications, a sufficient overlap between FOVs should be secured so that camera handoff can be executed successfully and automatically before the object of interest becomes untraceable or unidentifiable. In addition, most of the existing algorithms employ discrete solution space, which suffers from limited solution accuracy and high computational complexity due to the high dimension of sampled solution space. In this paper, we propose to perform the optimisation of the camera parameters in a continuous solution space. In addition, to incorporate the optimisation of coverage and sufficient overlapped FOVs, a weighted sum approach is utilised to translate a multiple objective optimisation problem into a single objective optimisation process in our previous work. Significantly improved handoff success rates are illustrated via experiments using typical office floor plans in comparison with Erdem and Sclaroff's method. Improved accuracy, enhanced robustness, completeness of the solution set, and reduced computational complexity are accomplished in comparison with our previous algorithms.
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