Publications (24)
Leveraging whole slide difficulty in Multiple Instance Learning to improve prostate cancer grading
Marie Arrivat, Rémy Peyret, Elsa Angelini +1
Multiple Instance Learning (MIL) has been widely applied in histopathology to classify Whole Slide Images (WSIs) with slide-level diagnoses. While the ground truth is established b…
Suggestive Annotation of Brain MR Images with Gradient-guided Sampling
Chengliang Dai, Shuo Wang, Yuanhan Mo +3
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. The success of m…
Few-Shot Airway-Tree Modeling using Data-Driven Sparse Priors
Ali Keshavarzi, Elsa Angelini
The lack of large annotated datasets in medical imaging is an intrinsic burden for supervised Deep Learning (DL) segmentation models. Few-shot learning approaches are cost-effectiv…
Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling
Chengliang Dai, Shuo Wang, Yuanhan Mo +4
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks. As a data-driven…
Cardiac Adipose Tissue Segmentation via Image-Level Annotations
Ziyi Huang, Yu Gan, Theresa Lye +5
Automatically identifying the structural substrates underlying cardiac abnormalities can potentially provide real-time guidance for interventional procedures. With the knowledge of…
SAPSAM - Sparsely Annotated Pathological Sign Activation Maps - A novel approach to train Convolutional Neural Networks on lung CT scans using binary labels only
Mario Zusag, Sujal Desai, Marcello Di Paolo +3
Chronic Pulmonary Aspergillosis (CPA) is a complex lung disease caused by infection with Aspergillus. Computed tomography (CT) images are frequently requested in patients with susp…
Push the Boundary of SAM: A Pseudo-label Correction Framework for Medical Segmentation
Ziyi Huang, Hongshan Liu, Haofeng Zhang +7
Segment anything model (SAM) has emerged as the leading approach for zero-shot learning in segmentation tasks, offering the advantage of avoiding pixel-wise annotations. It is part…
Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans
Xinzi He, Jia Guo, Xuzhe Zhang +9
Unsupervised learning-based medical image registration approaches have witnessed rapid development in recent years. We propose to revisit a commonly ignored while simple and well-e…
Transfer Learning from Partial Annotations for Whole Brain Segmentation
Chengliang Dai, Yuanhan Mo, Elsa Angelini +2
Brain MR image segmentation is a key task in neuroimaging studies. It is commonly conducted using standard computational tools, such as FSL, SPM, multi-atlas segmentation etc, whic…
Off-axis compressed holographic microscopy in low-light conditions
Marcio M. Marim, Elsa Angelini, J. C. Olivo-Marin +1
This Letter reports a demonstration of off-axis compressed holography in low-light level imaging conditions. An acquisition protocol relying on a single exposure of a randomly unde…
Co-Seg: An Image Segmentation Framework Against Label Corruption
Ziyi Huang, Haofeng Zhang, Andrew Laine +3
Supervised deep learning performance is heavily tied to the availability of high-quality labels for training. Neural networks can gradually overfit corrupted labels if directly tra…
Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation
Jun Chen, Guang Yang, Zhifan Gao +9
Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients,…
Tracking Intermittent Particles with Self-Learned Visual Features
Raphael Reme, Victor Piriou, Alison Hanson +5
In time-lapse fluorescence imaging, single-particle-tracking is a powerful tool to monitor the dynamics of objects of interest, and extract information about biological processes.…
Boundary-Emphasized Weight Maps for Distal Airway Segmentation
Ali Keshavarzi, Elsa Angelini
Automated airway segmentation from lung CT scans is vital for diagnosing and monitoring pulmonary diseases. Despite advancements, challenges like leakage, breakage, and class imbal…
Deep ContourFlow: Advancing Active Contours with Deep Learning
Antoine Habis, Vannary Meas-Yedid, Elsa Angelini +1
This paper introduces a novel approach that combines unsupervised active contour models with deep learning for robust and adaptive image segmentation. Indeed, traditional active co…
SINETRA: a Versatile Framework for Evaluating Single Neuron Tracking in Behaving Animals
Raphael Reme, Alasdair Newson, Elsa Angelini +2
Accurately tracking neuronal activity in behaving animals presents significant challenges due to complex motions and background noise. The lack of annotated datasets limits the eva…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
Compressed Sensing with off-axis frequency-shifting holography
Marcio Marim, Michael Atlan, Elsa Angelini +1
This work reveals an experimental microscopy acquisition scheme successfully combining Compressed Sensing (CS) and digital holography in off-axis and frequency-shifting conditions.…
MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling
Xuzhe Zhang, Yuhao Wu, Elsa Angelini +10
Robust segmentation is critical for deriving quantitative measures from large-scale, multi-center, and longitudinal medical scans. Manually annotating medical scans, however, is ex…
Dense Self-Supervised Learning for Medical Image Segmentation
Maxime Seince, Loic Le Folgoc, Luiz Augusto Facury de Souza +1
Deep learning has revolutionized medical image segmentation, but it relies heavily on high-quality annotations. The time, cost and expertise required to label images at the pixel-l…
BifDet: A 3D Bifurcation Detection Dataset for Airway-Tree Modeling
Ali Keshavarzi, Quentin Bouniot, Benjamin M. Smith +1
Thoracic Computed Tomography (CT) scans offer detailed insights into the intricate branching network of the airway tree, which is essential for understanding various respiratory di…
Curriculum Learning for Few-Shot Domain Adaptation in CT-based Airway Tree Segmentation
Maxime Jacovella, Ali Keshavarzi, Elsa Angelini
Despite advances with deep learning (DL), automated airway segmentation from chest CT scans continues to face challenges in segmentation quality and generalization across cohorts.…
Simultaneous Left Atrium Anatomy and Scar Segmentations via Deep Learning in Multiview Information with Attention
Guang Yang, Jun Chen, Zhifan Gao +13
Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to str…
Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction
Shuo Wang, Chengliang Dai, Yuanhan Mo +3
Gliomas are the most common malignant brain tumourswith intrinsic heterogeneity. Accurate segmentation of gliomas and theirsub-regions on multi-parametric magnetic resonance images…