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Found 32 papers, 6 papers with code
An FPGA smart camera implementation of segmentation models for drone wildfire imagery
Wildfires represent one of the most relevant natural disasters worldwide, due to their impact on various societal and environmental levels.
FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation
This contribution presents a deep learning method for the segmentation of prostate zones in MRI images based on U-Net using additive and feature pyramid attention modules, which can improve the workflow of prostate cancer detection and diagnosis.
Assessing the performance of deep learning-based models for prostate cancer segmentation using uncertainty scores
This study focuses on comparing deep learning methods for the segmentation and quantification of uncertainty in prostate segmentation from MRI images.
A metric learning approach for endoscopic kidney stone identification
The proposed Guided Deep Metric Learning approach is based on a novel architecture which was designed to learn data representations in an improved way.
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification
Few-shot learning is a challenging area of research that aims to learn new concepts with only a few labeled samples of data.
Isolated Sign Language Recognition based on Tree Structure Skeleton Images
We evaluated the effectiveness of our model on the Ankara University Turkish Sign Language (TSL) dataset, AUTSL, and a Mexican Sign Language (LSM) dataset.
Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations
Using PPs in the classification task enables case-based reasoning explanations for such output, thus making the model interpretable.
Deep learning-based image exposure enhancement as a pre-processing for an accurate 3D colon surface reconstruction
This contribution shows how an appropriate image pre-processing can improve a deep-learning based 3D reconstruction of colon parts.
Improving automatic endoscopic stone recognition using a multi-view fusion approach enhanced with two-step transfer learning
This contribution presents a deep-learning method for extracting and fusing image information acquired from different viewpoints, with the aim to produce more discriminant object features for the identification of the type of kidney stones seen in endoscopic images.
SUPRA: Superpixel Guided Loss for Improved Multi-modal Segmentation in Endoscopy
In this paper, we explore the domain generalisation technique to enable DL methods to be used in such scenarios.
Improved Kidney Stone Recognition Through Attention and Multi-View Feature Fusion Strategies
Moreover, in comparison to the state-of-the-art, the fusion of the deep features improved the overall results up to 11% in terms of kidney stone classification accuracy.
Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging
Endoscopy is the most widely used imaging technique for the diagnosis of cancerous lesions in hollow organs.
Boosting Kidney Stone Identification in Endoscopic Images Using Two-Step Transfer Learning
Finally, in comparison to models that are trained from scratch or by initializing ImageNet weights, the obtained results suggest that the two-step approach extracts features improving the identification of kidney stones in endoscopic images.
A semi-supervised Teacher-Student framework for surgical tool detection and localization
Therefore, in this paper we introduce a semi-supervised learning (SSL) framework in surgical tool detection paradigm which aims to mitigate the scarcity of training data and the data imbalance through a knowledge distillation approach.
Knowledge distillation with a class-aware loss for endoscopic disease detection
In this work, we leverage deep learning to develop a framework to improve the localization of difficult to detect lesions and minimize the missed detection rate.
Comparison of automatic prostate zones segmentation models in MRI images using U-net-like architectures
Prostate cancer is the second-most frequently diagnosed cancer and the sixth leading cause of cancer death in males worldwide.
MACFE: A Meta-learning and Causality Based Feature Engineering Framework
Feature engineering has become one of the most important steps to improve model prediction performance, and to produce quality datasets.
A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models
Endoscopy is the most widely used medical technique for cancer and polyp detection inside hollow organs.
Evaluating object detector ensembles for improving the robustness of artifact detection in endoscopic video streams
Extensive experiments show the superiority, in terms of mean average precision, of the ensemble approach over the individual models and previous works in the state of the art.
Computer Vision-based Characterization of Large-scale Jet Flames using a Synthetic Infrared Image Generation Approach
The results suggest that it is possible to realistically replicate the results for experiments carried out using both visible and infrared cameras.
Guided Deep Metric Learning
Deep Metric Learning (DML) methods have been proven relevant for visual similarity learning.
Impact of loss function in Deep Learning methods for accurate retinal vessel segmentation
The best average of Hausdorff distance and mean square error were obtained using the Nested U-Net with the Dice loss function, which had an average of 6. 32 and 0. 0241 respectively.
Interpretable Deep Learning Classifier by Detection of Prototypical Parts on Kidney Stones Images
Identifying the type of kidney stones can allow urologists to determine their formation cause, improving the early prescription of appropriate treatments to diminish future relapses.
Comparing feature fusion strategies for Deep Learning-based kidney stone identification
This contribution presents a deep-learning method for extracting and fusing image information acquired from different viewpoints with the aim to produce more discriminant object features.
On the in vivo recognition of kidney stones using machine learning
This pilot study compares the kidney stone recognition performances of six shallow machine learning methods and three deep-learning architectures which were tested with in-vivo images of the four most frequent urinary calculi types acquired with an endoscope during standard ureteroscopies.
Experimental Large-Scale Jet Flames' Geometrical Features Extraction for Risk Management Using Infrared Images and Deep Learning Segmentation Methods
This research work explores the application of deep learning models in an alternative approach that uses the semantic segmentation of jet fires flames to extract main geometrical attributes, relevant for fire risk assessments.
Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusion
While accurate tracking of surgical instruments in real-time plays a crucial role in minimally invasive computer-assisted surgeries, it is a challenging task to achieve, mainly due to 1) complex surgical environment, and 2) model design with both optimal accuracy and speed.
Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation
However, it is currently unclear whether the architecture of a model, its loss function, or the image type employed (visible, infrared, or fused) has the most impact on the fire segmentation results.
Comparing Machine Learning based Segmentation Models on Jet Fire Radiation Zones
One such characterization would be the segmentation of different radiation zones within the flame, so this paper presents an exploratory research regarding several traditional computer vision and Deep Learning segmentation approaches to solve this specific problem.
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction
This module helps to improve the accuracy performance by allowing the similarity function, given by the metric learning method, to have more discriminative features for the classification.
A Bop and Beyond: A Second Order Optimizer for Binarized Neural Networks
Current techniques for weight-updating use the same approaches as traditional Neural Networks (NNs) with the extra requirement of using an approximation to the derivative of the sign function - as it is the Dirac-Delta function - for back-propagation; thus, efforts are focused adapting full-precision techniques to work on BNNs.
Assessing YOLACT++ for real time and robust instance segmentation of medical instruments in endoscopic procedures
Image-based tracking of laparoscopic instruments plays a fundamental role in computer and robotic-assisted surgeries by aiding surgeons and increasing patient safety.
