Search Results for author:
Found 8 papers, 4 papers with code
Few-shot learning for COVID-19 Chest X-Ray Classification with Imbalanced Data: An Inter vs. Intra Domain Study
For this, we propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance.
Multi-Label Logo Recognition and Retrieval based on Weighted Fusion of Neural Features
While the current state of the art for logo classification addresses the problem as a multi-class task focusing on a single characteristic, logos can have several simultaneous labels, such as different colors.
UMLS-ChestNet: A deep convolutional neural network for radiological findings, differential diagnoses and localizations of COVID-19 in chest x-rays
In this work we present a method for the detection of radiological findings, their location and differential diagnoses from chest x-rays.
BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients
This paper describes BIMCV COVID-19+, a large dataset from the Valencian Region Medical ImageBank (BIMCV) containing chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19+ patients along with their radiological findings and locations, pathologies, radiological reports (in Spanish), DICOM metadata, Polymerase chain reaction (PCR), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) diagnostic antibody tests.
A holistic approach to polyphonic music transcription with neural networks
We present a framework based on neural networks to extract music scores directly from polyphonic audio in an end-to-end fashion.
PadChest: A large chest x-ray image dataset with multi-label annotated reports
We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports.
Learning Eligibility in Cancer Clinical Trials using Deep Neural Networks
A text classifier was trained using deep neural networks, with pre-trained word-embeddings as inputs, to predict whether or not short free-text statements describing clinical information were considered eligible.
MirBot: A collaborative object recognition system for smartphones using convolutional neural networks
This dataset grows continuously thanks to the users' feedback, and is publicly available for research.
