0000000000605787

AUTHOR

Daniela Besozzi

showing 2 related works from this author

USE-Net: Incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets

2019

Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since tumor's frequency and severity differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study ev…

FOS: Computer and information sciences0209 industrial biotechnologyComputer Science - Machine LearningGeneralizationComputer scienceComputer Vision and Pattern Recognition (cs.CV)Cognitive NeuroscienceComputer Science - Computer Vision and Pattern RecognitionConvolutional neural network02 engineering and technologyConvolutional neural networkMachine Learning (cs.LG)Image (mathematics)Prostate cancer020901 industrial engineering & automationArtificial IntelligenceProstate0202 electrical engineering electronic engineering information engineeringmedicineMedical imagingAnatomical MRISegmentationBlock (data storage)Prostate cancermedicine.diagnostic_testSettore INF/01 - Informaticabusiness.industryAnatomical MRI; Convolutional neural networks; Cross-dataset generalization; Prostate cancer; Prostate zonal segmentation; USE-NetINF/01 - INFORMATICAMagnetic resonance imagingPattern recognitionUSE-Netmedicine.diseaseComputer Science Applicationsmedicine.anatomical_structureCross-dataset generalizationFeature (computer vision)Prostate zonal segmentation020201 artificial intelligence & image processingConvolutional neural networksArtificial intelligencebusinessEncoder
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Strategies for structuring interdisciplinary education in Systems Biology: an European perspective

2016

Systems Biology is an approach to biology and medicine that has the potential to lead to a better understanding of how biological properties emerge from the interaction of genes, proteins, molecules, cells and organisms. The approach aims at elucidating how these interactions govern biological function by employing experimental data, mathematical models and computational simulations. As Systems Biology is inherently multidisciplinary, education within this field meets numerous hurdles including departmental barriers, availability of all required expertise locally, appropriate teaching material and example curricula. As university education at the Bachelor’s level is traditionally built upon…

0301 basic medicineEngineeringSystems biologymedia_common.quotation_subjectStructuringGeneral Biochemistry Genetics and Molecular BiologyArticleEducation03 medical and health sciences0302 clinical medicineExcellenceMultidisciplinary approachDrug DiscoveryComputingMilieux_COMPUTERSANDEDUCATIONLife ScienceSystems and Synthetic BiologyInnovation/dk/atira/pure/sustainabledevelopmentgoals/industry_innovation_and_infrastructureCurriculummedia_commonVLAGFlexibility (engineering)Systeem en Synthetische BiologieScience & TechnologyManagement sciencebusiness.industry4. EducationApplied MathematicsINF/01 - INFORMATICAGAPGénéralitésSystems Biology Training and education3. Good healthComputer Science Applications030104 developmental biologyAction (philosophy)Modeling and Simulationand InfrastructureSDG 9 - Industry Innovation and InfrastructureMathematical & Computational BiologySystems biologybusinessDisciplineSDG 9 - IndustryLife Sciences & Biomedicine030217 neurology & neurosurgery
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