22 citations · 34 across the 7 of their papers we have counts for
7 papers
Interactive Whole Slide Images for RL-based Tumour Segmentation
Mohamad Mohamad, Francesco Ponzio, Maxime Gassier +2
Whole-slide image (WSI) analysis remains computationally challenging due to the extremely large spatial resolution of slides and the sparse distribution of tumour regions. We propo…
Mode-Dependent Rectification for Stable PPO Training
Mohamad Mohamad, Francesco Ponzio, Xavier Descombes
Mode-dependent architectural components (layers that behave differently during training and evaluation, such as Batch Normalization or dropout) are commonly used in visual reinforc…
Renal Cell Carcinoma subtyping: learning from multi-resolution localization
Mohamad Mohamad, Francesco Ponzio, Santa Di Cataldo +2
Renal Cell Carcinoma is typically asymptomatic at the early stages for many patients. This leads to a late diagnosis of the tumor, where the curability likelihood is lower, and mak…
VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the Edge
Alessio Mascolini, Sebastiano Gaiardelli, Francesco Ponzio +5
Detecting complex anomalies on massive amounts of data is a crucial task in Industry 4.0, best addressed by deep learning. However, available solutions are computationally demandin…
Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0
Luigi Capogrosso, Alessio Mascolini, Federico Girella +10
Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. A…
Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations: a COVID-19 case-study
Alessio Mascolini, Dario Cardamone, Francesco Ponzio +2
Computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper we prese…
W2WNet: a two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality
Francesco Ponzio, Enrico Macii, Elisa Ficarra +1
Convolutional Neural Networks (CNNs) are supposed to be fed with only high-quality annotated datasets. Nonetheless, in many real-world scenarios, such high quality is very hard to…