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Found 9 papers, 2 papers with code
A Topological Machine Learning Pipeline for Classification
In this work, we develop a pipeline that associates Persistence Diagrams to digital data via the most appropriate filtration for the type of data considered.
Alzheimer Disease Detection from Raman Spectroscopy of the Cerebrospinal Fluid via Topological Machine Learning
The cerebrospinal fluid (CSF) of 19 subjects who received a clinical diagnosis of Alzheimer's disease (AD) as well as of 5 pathological controls have been collected and analysed by Raman spectroscopy (RS).
Are We Using Autoencoders in a Wrong Way?
Autoencoders are certainly among the most studied and used Deep Learning models: the idea behind them is to train a model in order to reconstruct the same input data.
High Dynamic Range Imaging via Visual Attention Modules
Thanks to High Dynamic Range (HDR) imaging methods, the scope of photography has seen profound changes recently.
Supervised Image Segmentation for High Dynamic Range Imaging
However, unfortunately, most methods for generating an HDR image from Multi-Exposure images only concentrate on how to combine different exposures and do not have any focus on choosing the best details of each image.
Improving plant disease classification by adaptive minimal ensembling
Our method was tested on PlantVillage, a public reference dataset used for benchmarking models' performances for crop disease diagnostic, considering both its original and augmented versions.
Ranked #1 on
Image Classification
on PlantVillage
Revisiting ensembling for improving the performance of deep learning models
Ensembling is a very well-known strategy consisting in fusing several different models to achieve a new model for classification or regression tasks.
Efficient Adaptive Ensembling for Image Classification
In recent times, with the exception of sporadic cases, the trend in Computer Vision is to achieve minor improvements compared to considerable increases in complexity.
Ranked #1 on
Image Classification
on Oxford-IIIT Pets
From Human Mesenchymal Stromal Cells to Osteosarcoma Cells Classification by Deep Learning
Our contribution to the classification of osteosarcoma cells is made as follows: a DL approach is applied to discriminate human Mesenchymal Stromal Cells (MSCs) from osteosarcoma cells and to classify the different cell populations under investigation.
