Search Results for author:
Found 45 papers, 20 papers with code
Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection
Besides, we initiate the research on domain adaptation for instrument segmentation in cataract surgery by evaluating cross-domain instrument segmentation performance in cataract surgery videos.
Predicting Postoperative Intraocular Lens Dislocation in Cataract Surgery via Deep Learning
The Pearson correlation and t-test results reveal significant correlations between lens unfolding delay and lens rotation and significant differences between the intra-operative rotations stability of four groups of lenses.
DeepPyramid+: Medical Image Segmentation using Pyramid View Fusion and Deformable Pyramid Reception
We propose a network architecture, DeepPyramid+, which addresses diverse challenges encountered in medical image and surgical video segmentation.
Correlation-aware active learning for surgery video segmentation
Active Learning (AL) is a popular approach that can help to reduce this burden by iteratively selecting images for annotation to improve the model performance.
Learning Super-Resolution Ultrasound Localization Microscopy from Radio-Frequency Data
Ultrasound Localization Microscopy (ULM) enables imaging of vascular structures in the micrometer range by accumulating contrast agent particle locations over time.
RF-ULM: Ultrasound Localization Microscopy Learned from Radio-Frequency Wavefronts
However, our study uncovers an enormous potential: The process of delay-and-sum beamforming leads to an irreversible reduction of Radio-Frequency (RF) channel data, while its implications for localization remain largely unexplored.
Hyperbolic Random Forests
To make hyperbolic random forests work on multi-class data and imbalanced experiments, we furthermore outline a new method for combining classes based on their lowest common ancestor and a class-balanced version of the large-margin loss.
StofNet: Super-resolution Time of Flight Network
Time of Flight (ToF) is a prevalent depth sensing technology in the fields of robotics, medical imaging, and non-destructive testing.
Domain Adaptation for Medical Image Segmentation using Transformation-Invariant Self-Training
However, the unreliability of pseudo labels can hinder the capability of self-training techniques to induce abstract representation from the unlabeled target dataset, especially in the case of large distribution gaps.
A reinforcement learning approach for VQA validation: an application to diabetic macular edema grading
As such, validation of machine learning models represents an important aspect and yet, most methods are only validated in a limited way.
Localized Questions in Medical Visual Question Answering
Our code and data are available at https://github. com/sergiotasconmorales/locvqa.
Geometric Ultrasound Localization Microscopy
Contrast-Enhanced Ultra-Sound (CEUS) has become a viable method for non-invasive, dynamic visualization in medical diagnostics, yet Ultrasound Localization Microscopy (ULM) has enabled a revolutionary breakthrough by offering ten times higher resolution.
Learning How To Robustly Estimate Camera Pose in Endoscopic Videos
Purpose: Surgical scene understanding plays a critical role in the technology stack of tomorrow's intervention-assisting systems in endoscopic surgeries.
Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery
Detecting so-called out-of-distribution (OoD) samples is crucial in safety-critical applications such as robotically guided retinal microsurgery, where distances between the instrument and the retina are derived from sequences of 1D images that are acquired by an instrument-integrated optical coherence tomography (iiOCT) probe.
Full or Weak annotations? An adaptive strategy for budget-constrained annotation campaigns
However, for any new domain application looking to use weak supervision, the dataset builder still needs to define a strategy to distribute full segmentation and other weak annotations.
Logical Implications for Visual Question Answering Consistency
Despite considerable recent progress in Visual Question Answering (VQA) models, inconsistent or contradictory answers continue to cast doubt on their true reasoning capabilities.
Stochastic Segmentation with Conditional Categorical Diffusion Models
Semantic segmentation has made significant progress in recent years thanks to deep neural networks, but the common objective of generating a single segmentation output that accurately matches the image's content may not be suitable for safety-critical domains such as medical diagnostics and autonomous driving.
ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection
In this work, we focus on applying our method to the detection of AGN candidates in a Sloan Digital Sky Survey galaxy sample, since the identification and classification of Active Galactic Nuclei (AGN) in the optical band still remains a challenging task in extragalactic astronomy.
DeepPyramid: Enabling Pyramid View and Deformable Pyramid Reception for Semantic Segmentation in Cataract Surgery Videos
Semantic segmentation in cataract surgery has a wide range of applications contributing to surgical outcome enhancement and clinical risk reduction.
Consistency-preserving Visual Question Answering in Medical Imaging
Visual Question Answering (VQA) models take an image and a natural-language question as input and infer the answer to the question.
Data Invariants to Understand Unsupervised Out-of-Distribution Detection
Motivated by a simple thought experiment, we propose a characterization of U-OOD based on the invariants of the training dataset.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
A Positive/Unlabeled Approach for the Segmentation of Medical Sequences using Point-Wise Supervision
While most methods of this kind assume that the proportion of positive samples in the data is known a-priori, we introduce a novel self-supervised method to estimate this prior efficiently by combining a Bayesian estimation framework and new stopping criteria.
CataNet: Predicting remaining cataract surgery duration
Cataract surgery is a sight saving surgery that is performed over 10 million times each year around the world.
Volumetric Quantitative Ablation Margins for Assessment of Ablation Completeness in Thermal Ablation of Liver Tumors
For subcapsular tumors, the underestimation of tumor coverage by the ablation volume when applying an unadjusted QAM method was confirmed, supporting the benefits of using the proposed algorithm for QAM computation in these cases.
Stereo Correspondence and Reconstruction of Endoscopic Data Challenge
The stereo correspondence and reconstruction of endoscopic data sub-challenge was organized during the Endovis challenge at MICCAI 2019 in Shenzhen, China.
Surgical Data Science -- from Concepts toward Clinical Translation
We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.
A Question-Centric Model for Visual Question Answering in Medical Imaging
Deep learning methods have proven extremely effective at performing a variety of medical image analysis tasks.
Fused Detection of Retinal Biomarkers in OCT Volumes
In this context, we present a method that automatically predicts the presence of biomarkers in OCT cross-sections by incorporating information from the entire volume.
Concept-Centric Visual Turing Tests for Method Validation
To do this, we make use of a Twenty Questions paradigm whereby we use a probabilistic model to characterize the method's capacity to grasp task-specific concepts, and we introduce a strategy to sequentially query the method according to its previous answers.
Deep Multi Label Classification in Affine Subspaces
We evaluate the method using two MLC medical imaging datasets and show a large performance increase compared to previous multi-label frameworks.
Discovering General-Purpose Active Learning Strategies
We propose a general-purpose approach to discovering active learning (AL) strategies from data.
Iterative multi-path tracking for video and volume segmentation with sparse point supervision
Our object model is then used in a graph-based optimization problem that takes into account all provided locations and the image data in order to infer the complete pixel-wise segmentation.
Supervised Machine Learning for Analysing Spectra of Exoplanetary Atmospheres
Machine learning has previously been used to determine which molecules to include in the model, but the retrieval itself was still performed using standard methods.
Earth and Planetary Astrophysics Atmospheric and Oceanic Physics Data Analysis, Statistics and Probability
Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery
The paper presents a comparative validation study of different vision-based methods for instrument segmentation and tracking in the context of robotic as well as conventional laparoscopic surgery.
Simultaneous Recognition and Pose Estimation of Instruments in Minimally Invasive Surgery
An additional advantage of our approach is that instrument detection at test time is achieved while avoiding the need for scale-dependent sliding window evaluation.
Expected exponential loss for gaze-based video and volume ground truth annotation
Many recent machine learning approaches used in medical imaging are highly reliant on large amounts of image and ground truth data.
Pathological OCT Retinal Layer Segmentation using Branch Residual U-shape Networks
The automatic segmentation of retinal layer structures enables clinically-relevant quantification and monitoring of eye disorders over time in OCT imaging.
Learning Active Learning from Data
In this paper, we suggest a novel data-driven approach to active learning (AL).
RetiNet: Automatic AMD identification in OCT volumetric data
In much the same way, acquiring ground truth information for each cross-section is expensive and time consuming.
Geometry in Active Learning for Binary and Multi-class Image Segmentation
Our approach combines geometric smoothness priors in the image space with more traditional uncertainty measures to estimate which pixels or voxels are the most informative, and thus should to be annotated next.
Active Learning for Delineation of Curvilinear Structures
Many recent delineation techniques owe much of their increased effectiveness to path classification algorithms that make it possible to distinguish promising paths from others.
Introducing Geometry in Active Learning for Image Segmentation
We propose an Active Learning approach to training a segmentation classifier that exploits geometric priors to streamline the annotation process in 3D image volumes.
Reconstructing Evolving Tree Structures in Time Lapse Sequences
We propose an approach to reconstructing tree structures that evolve over time in 2D images and 3D image stacks such as neuronal axons or plant branches.
Fast Object Detection with Entropy-Driven Evaluation
Cascade-style approaches to implementing ensemble classifiers can deliver significant speed-ups at test time.
Active Testing for Face Detection and Localization
We provide a novel search technique, which uses a hierarchical model and a mutual information gain heuristic to efficiently prune the search space when localizing faces in images.
