2018/10/22 by Maxim Pisov, Gleb Makarchuk, Pisov, Maxim +9 · 6 citations
Computer Science · Neuroscience · #AI in cancer detection #Artificial intelligence #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Contextual image classification #Convolutional neural network #Deep learning #Digital Imaging for Blood Diseases #FOS: Computer and information sciences #Feature (linguistics) #Feature extraction #Feature vector #Image (mathematics) #Image retrieval #Machine learning #Metric (unit) #Multi-task learning #Pattern recognition (psychology) #Similarity (geometry) #Task (project management) #cs.CV
paper · pdf · doi:10.48550/arxiv.1810.09369
published in arXiv (Cornell University) (Cornell University)
arxiv created 2018/10/22 · openalex publication_date 2018/10/22 · arxiv updated 2018/10/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
Classification-based image retrieval systems are built by training convolutional neural networks (CNNs) on a relevant classification problem and using the distance in the resulting feature space as a similarity metric. However, in practical applications, it is often desirable to have representations which take into account several aspects of the data (e.g., brain tumor type and its localization). In our work, we extend the classification-based approach with multitask learning: we train a CNN on brain MRI scans with heterogeneous labels and implement a corresponding tumor image retrieval system. We validate our approach on brain tumor data which contains information about tumor types, shapes and localization. We show that our method allows us to build representations that contain more relevant information about tumors than single-task classification-based approaches.