280 citations · 1.7k across the 60 of their papers we have counts for
66 papers
WiseLVAM: A Novel Framework For Left Ventricle Automatic Measurements
Durgesh Kumar Singh, Qing Cao, Sarina Thomas +3
Clinical guidelines recommend performing left ventricular (LV) linear measurements in B-mode echocardiographic images at the basal level -- typically at the mitral valve leaflet ti…
SuperCM: Improving Semi-Supervised Learning and Domain Adaptation through differentiable clustering
Durgesh Singh, Ahcène Boubekki, Robert Jenssen +1
Semi-Supervised Learning (SSL) and Unsupervised Domain Adaptation (UDA) enhance the model performance by exploiting information from labeled and unlabeled data. The clustering assu…
Supercm: Revisiting Clustering for Semi-Supervised Learning
Durgesh Singh, Ahcene Boubekki, Robert Jenssen +1
The development of semi-supervised learning (SSL) has in recent years largely focused on the development of new consistency regularization or entropy minimization approaches, often…
EnLVAM: Enhanced Left Ventricle Linear Measurements Utilizing Anatomical Motion Mode
Durgesh K. Singh, Ahcene Boubekki, Qing Cao +3
Linear measurements of the left ventricle (LV) in the Parasternal Long Axis (PLAX) view using B-mode echocardiography are crucial for cardiac assessment. These involve placing 4-6…
Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders
Rogelio A Mancisidor, Robert Jenssen, Shujian Yu +1
Multimodal learning with variational autoencoders (VAEs) requires estimating joint distributions to evaluate the evidence lower bound (ELBO). Current methods, the product and mixtu…
Cauchy-Schwarz Divergence Information Bottleneck for Regression
Shujian Yu, Xi Yu, Sigurd Løkse +2
The information bottleneck (IB) approach is popular to improve the generalization, robustness and explainability of deep neural networks. Essentially, it aims to find a minimum suf…
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering
Daniel J. Trosten, Sigurd Løkse, Robert Jenssen +1
Self-supervised learning is a central component in recent approaches to deep multi-view clustering (MVC). However, we find large variations in the development of self-supervision-b…
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
Daniel J. Trosten, Rwiddhi Chakraborty, Sigurd Løkse +3
Distance-based classification is frequently used in transductive few-shot learning (FSL). However, due to the high-dimensionality of image representations, FSL classifiers are pron…
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making
Shujian Yu, Hongming Li, Sigurd Løkse +2
The Cauchy-Schwarz (CS) divergence was developed by Príncipe et al. in 2000. In this paper, we extend the classic CS divergence to quantify the closeness between two conditional di…
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
Srishti Gautam, Ahcene Boubekki, Stine Hansen +4
The need for interpretable models has fostered the development of self-explainable classifiers. Prior approaches are either based on multi-stage optimization schemes, impacting the…
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
Kristoffer Knutsen Wickstrøm, Eirik Agnalt Østmo, Keyur Radiya +3
Deep learning-based approaches for content-based image retrieval (CBIR) of CT liver images is an active field of research, but suffers from some critical limitations. First, they a…
Principle of Relevant Information for Graph Sparsification
Shujian Yu, Francesco Alesiani, Wenzhe Yin +2
Graph sparsification aims to reduce the number of edges of a graph while maintaining its structural properties. In this paper, we propose the first general and effective informatio…
The Kernelized Taylor Diagram
Kristoffer Wickstrøm, J. Emmanuel Johnson, Sigurd Løkse +4
This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely u…
BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck
Kaizhong Zheng, Shujian Yu, Baojuan Li +2
Developing a new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus. Recently, machi…
Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series
Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen +1
The lack of labeled data is a key challenge for learning useful representation from time series data. However, an unsupervised representation framework that is capable of producing…
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels
Stine Hansen, Srishti Gautam, Robert Jenssen +1
Recent work has shown that label-efficient few-shot learning through self-supervision can achieve promising medical image segmentation results. However, few-shot segmentation model…
Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation
Srishti Gautam, Marina M. -C. Höhne, Stine Hansen +2
The recent trend of integrating multi-source Chest X-Ray datasets to improve automated diagnostics raises concerns that models learn to exploit source-specific correlations to impr…
RELAX: Representation Learning Explainability
Kristoffer K. Wickstrøm, Daniel J. Trosten, Sigurd Løkse +4
Despite the significant improvements that representation learning via self-supervision has led to when learning from unlabeled data, no methods exist that explain what influences t…
Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
Multi-modality data is becoming readily available in remote sensing (RS) and can provide complementary information about the Earth's surface. Effective fusion of multi-modal inform…
Discriminative Multimodal Learning via Conditional Priors in Generative Models
Rogelio A. Mancisidor, Michael Kampffmeyer, Kjersti Aas +1
Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data. These two learning mechanisms can,…
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation
Srishti Gautam, Marina M. -C. Höhne, Stine Hansen +2
Current machine learning models have shown high efficiency in solving a wide variety of real-world problems. However, their black box character poses a major challenge for the unde…
On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit
Óscar Escudero-Arnanz, Joaquín Rodríguez-Álvarez, Karl Øyvind Mikalsen +2
The acquisition of Antimicrobial Multidrug Resistance (AMR) in patients admitted to the Intensive Care Units (ICU) is a major global concern. This study analyses data in the form o…
Reconsidering Representation Alignment for Multi-view Clustering
Daniel J. Trosten, Sigurd Løkse, Robert Jenssen +1
Aligning distributions of view representations is a core component of today's state of the art models for deep multi-view clustering. However, we identify several drawbacks with na…
Measuring Dependence with Matrix-based Entropy Functional
Shujian Yu, Francesco Alesiani, Xi Yu +2
Measuring the dependence of data plays a central role in statistics and machine learning. In this work, we summarize and generalize the main idea of existing information-theoretic…