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Found 16 papers, 2 papers with code
A Federated Learning Framework for Stenosis Detection
Our results showed that the FL framework does not substantially affects clients 2 performance, which already achieved good performance with local training; for client 1, instead, FL framework increases the performance with respect to local model of +3. 76%, +17. 21% and +10. 80%, respectively, reaching P rec = 73. 56, Rec = 67. 01 and F1 = 70. 13.
A store-and-forward cloud-based telemonitoring system for automatic assessing dysarthria evolution in neurological diseases from video-recording analysis
This architecture, called facial landmark Mask RCNN, aims at locating facial landmarks as a prior for assessing the orofacial functions related to speech and examining dysarthria evolution in neurological diseases.
Deep learning-based approaches for human motion decoding in smart walkers for rehabilitation
Promising results were attained for early action detection as a human motion decoding strategy, with enhancements in the focus of the proposed architectures.
Learning-Based Keypoint Registration for Fetoscopic Mosaicking
This surgery is minimally invasive and relies on fetoscopy.
Autonomous Intraluminal Navigation of a Soft Robot using Deep-Learning-based Visual Servoing
Navigation inside luminal organs is an arduous task that requires non-intuitive coordination between the movement of the operator's hand and the information obtained from the endoscopic video.
Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings
For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips.
A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis
This paper ends with a critical summary of the current state of the art on DL algorithms for fetal US image analysis and a discussion on current challenges that have to be tackled by researchers working in the field to translate the research methodology into the actual clinical practice.
Real-Time Human Pose Estimation on a Smart Walker using Convolutional Neural Networks
We present a novel approach to patient monitoring and data-driven human-in-the-loop control in the context of smart walkers.
How can we learn (more) from challenges? A statistical approach to driving future algorithm development
Challenges have become the state-of-the-art approach to benchmark image analysis algorithms in a comparative manner.
FetReg: Placental Vessel Segmentation and Registration in Fetoscopy Challenge Dataset
Through the \textit{Fetoscopic Placental Vessel Segmentation and Registration (FetReg)} challenge, we present a large-scale multi-centre dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms for the fetal environment with a focus on creating drift-free mosaics from long duration fetoscopy videos.
A transfer-learning approach for lesion detection in endoscopic images from the urinary tract
In this work we study the implementation of 3 different Convolutional Neural Networks (CNNs), using a 2-steps training strategy, to classify images from the urinary tract with and without lesions.
Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy
Of these, two architectures are taken as core-models, namely U-Net based in residual blocks($m_1$) and Mask-RCNN($m_2$), which are fed with single still-frames $I(t)$.
A Lumen Segmentation Method in Ureteroscopy Images based on a Deep Residual U-Net architecture
For the training of these networks, we analyze the use of two different color spaces: gray-scale and RGB data images.
Comparison of different CNNs for breast tumor classification from ultrasound images
The best performance was obtained by fine tuning VGG-16, with an accuracy of 0. 919 and an AUC of 0. 934.
Preterm infants' pose estimation with spatio-temporal features
Assessment of the proposed framework is performed through a comprehensive study with sixteen depth videos acquired in the actual clinical practice from sixteen preterm infants (the babyPose dataset).
Preterm infants' limb-pose estimation from depth images using convolutional neural networks
Preterm infants' limb-pose estimation is a crucial but challenging task, which may improve patients' care and facilitate clinicians in infant's movements monitoring.
