Search Results for author:
Found 17 papers, 9 papers with code
On the detection of Out-Of-Distribution samples in Multiple Instance Learning
While significant efforts have been devoted to OOD detection in classical supervised settings, the context of weakly supervised learning, particularly the Multiple Instance Learning (MIL) framework, remains under-explored.
Multiple Instance Learning
Out of Distribution (OOD) Detection
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Spatio-Temporal Analysis of Patient-Derived Organoid Videos Using Deep Learning for the Prediction of Drug Efficacy
In particular, PDOs are attracting interest in the field of Functional Precision Medicine (FPM), which is based upon an ex-vivo drug test in which living tumor cells (such as PDOs) from a specific patient are exposed to a panel of anti-cancer drugs.
Structured State Space Models for Multiple Instance Learning in Digital Pathology
In such cases, a whole slide image is modelled as a collection of tissue patches to be aggregated and classified.
Artifact Removal in Histopathology Images
In the clinical setting of histopathology, whole-slide image (WSI) artifacts frequently arise, distorting regions of interest, and having a pernicious impact on WSI analysis.
Region-guided CycleGANs for Stain Transfer in Whole Slide Images
We present a use case on whole slide images, where an IHC stain provides an experimentally generated signal for metastatic cells.
Self-Supervised Representation Learning using Visual Field Expansion on Digital Pathology
In this study, we propose a novel generative framework that can learn powerful representations for such tiles by learning to plausibly expand their visual field.
Exploring Deep Registration Latent Spaces
Explainability of deep neural networks is one of the most challenging and interesting problems in the field.
Self-Supervised Nuclei Segmentation in Histopathological Images Using Attention
We show that the identification of the magnification level for tiles can generate a preliminary self-supervision signal to locate nuclei.
AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia
Chest computed tomography (CT) is widely used for the management of Coronavirus disease 2019 (COVID-19) pneumonia because of its availability and rapidity.
Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation
Image registration and segmentation are the two most studied problems in medical image analysis.
An Artificial Intelligence-Based System to Assess Nutrient Intake for Hospitalised Patients
Regular monitoring of nutrient intake in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition.
Self-Attention and Ingredient-Attention Based Model for Recipe Retrieval from Image Queries
Direct computer vision based-nutrient content estimation is a demanding task, due to deformation and occlusions of ingredients, as well as high intra-class and low inter-class variability between meal classes.
U-ReSNet: Ultimate coupling of Registration and Segmentation with deep Nets
We evaluated the proposed architecture using the publicly available OASIS 3 dataset, measuring the dice coefficient metric for both registration and segmentation tasks.
An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised Patients
Regular nutrient intake monitoring in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition (DRM).
Linear and Deformable Image Registration with 3D Convolutional Neural Networks
Image registration and in particular deformable registration methods are pillars of medical imaging.
Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks
In this study, we propose the use of a deep purely convolutional neural network for the semantic segmentation of ILD patterns, as the basic component of a computer aided diagnosis (CAD) system for ILDs.
Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis
In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem.
