Titlebook: Image Analysis and Processing - ICIAP 2023 Workshops; Udine, Italy, Septem Gian Luca Foresti,Andrea Fusiello,Edwin Hancock Conference proce

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Breast Mass Detection and?Classification Using Transfer Learning on?OPTIMAM Dataset Through RadImageels. Among the publicly available RadImageNet weights, DenseNet-121 coupled with the yolov5m model gives 0.718 mean average precision(mAP) at 0.5 IoU threshold and a True Positive Rate (TPR) of 0.97 at 0.85 False Positives Per Image (FPPI). For the classification task, we implement a transfer learni

針葉樹

Grading and Staging of Bladder Tumors Using Radiomics Analysis in Magnetic Resonance Imagingruction of the predictive model. The performance in the discrimination between LG and HG lesions, with an AUROC of 0.84 (95% C.I. between 0.71 and 0.98), sensitivity of 65.6%, specificity of 81.5%, with p?

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Combined Data Augmentation for?HEp-2 Cells Image Classificationle labels during generation to enhance versatility. Extensive experiments were conducted with the largest publicly available dataset of HEp-2 cell images, the . dataset. The performance of traditional and generative data augmentation techniques were compared while investigating potential synergies b

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幾何學(xué)家

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A Multi-dimensional Joint ICA Model with Gaussian Copulabased studies on neuroimaging data have successfully modeled independent sources with a logistic distribution, providing robust and replicable results across modalities. This is because neuroimaging data often consists of rapid fluctuations around a baseline, resulting in super-Gaussian distribution

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Federated Learning for?Data and?Model Heterogeneity in?Medical Imagingdge distillation and a symmetric loss to minimize the heterogeneity and its impact on the model performance. Knowledge distillation is used to solve the problem of model heterogeneity, and symmetric loss tackles with the data and label heterogeneity. We evaluate our method on the medical datasets to

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Experience Sharing and?Human-in-the-Loop Optimization for?Federated Robot Navigation Recommendationoptimization, such as human-generated suggestions and advice, to provide the robotic agent with context-aware navigation recommendations is introduced in this paper. More specifically, the conceptual architecture of a robot navigation recommender system (RoboRecSys) is proposed to provide the agent