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Fluorescence microscopy has allowed studying dynamical biological processes in vivo with an ever increasing accuracy. Nonetheless, the physically inherent resolution limits impede the study of very dynamical intracellular processes such as microtubule dynamics. One way to overcome this limited resolution is to reconstruct the underlying object dynamics from the image data by using Bayesian statistics...
Histopathology image classification can provide automated support towards cancer diagnosis. In this paper, we present a transfer learning-based approach for histopathology image classification. We first represent the image feature by Fisher Vector (FV) encoding of local features that are extracted using the Convolutional Neural Network (CNN) model pretrained on ImageNet. Next, to better transfer the...
We introduce a novel model-based generator that produces biologically grounded synthetic volumes of the cerebrovasculature. Our models are synthesized stochastically, according to the biological characteristics of venule arborescence in the human collateral sulcus. Each synthetic volume produced is individually unique, yet representative of this cerebral region. As the locations and characteristics...
Functional and structural connectivity convey different information about the brain. The integration of these different approaches is receiving growing attention from the research community, as it can shed new light on brain functions. This manuscript proposes a constrained autoregressive model with different lag-orders generating an “effective” connectivity matrix which models the structural connectivity...
Despite the common invisibility of cancerous lesions in transrectal ultrasound (TRUS), TRUS-guided random biopsy is considered the gold standard to diagnose prostate cancer. Pre-interventional magnetic resonance imaging (MRI) has been shown to improve the detection of malignancies but fast and accurate MRI/TRUS registration for multi-modal biopsy guidance remains challenging. In this work, we derive...
Objectives: Using in silico simulations from histopathological cancer prostate specimen, the objectives were to identify the total dose corresponding to various fractionations necessary to destroy the tumor cells (50% to 99.9%) and to assess the impact of the Gleason score on those doses.
Automated cell detection is a critical step for a number of computer-assisted pathology related image analysis algorithm. However, automated cell detection is complicated due to the variable cytomorphological and histological factors associated with each cell. In order to efficiently resolve the challenge of automated cell detection, deep learning strategies are widely applied and have recently been...
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