Search Results for author:
Found 49 papers, 20 papers with code
DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical Images
DynaMMo achieves this without compromising performance, offering a cost-effective solution for continual learning in medical applications.
PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentation
In this work, we propose a parameter-efficient multi-modal adaptation (PEMMA) framework for lightweight upgrading of a transformer-based segmentation model trained only on CT scans to also incorporate PET scans.
Envisioning MedCLIP: A Deep Dive into Explainability for Medical Vision-Language Models
Explaining Deep Learning models is becoming increasingly important in the face of daily emerging multimodal models, particularly in safety-critical domains like medical imaging.
EDUE: Expert Disagreement-Guided One-Pass Uncertainty Estimation for Medical Image Segmentation
Deploying deep learning (DL) models in medical applications relies on predictive performance and other critical factors, such as conveying trustworthy predictive uncertainty.
MedPromptX: Grounded Multimodal Prompting for Chest X-ray Diagnosis
Chest X-ray images are commonly used for predicting acute and chronic cardiopulmonary conditions, but efforts to integrate them with structured clinical data face challenges due to incomplete electronic health records (EHR).
TiBiX: Leveraging Temporal Information for Bidirectional X-ray and Report Generation
To address this, we propose TiBiX: Leveraging Temporal information for Bidirectional X-ray and Report Generation.
FissionFusion: Fast Geometric Generation and Hierarchical Souping for Medical Image Analysis
The scarcity of well-annotated medical datasets requires leveraging transfer learning from broader datasets like ImageNet or pre-trained models like CLIP.
HuLP: Human-in-the-Loop for Prognosis
This paper introduces HuLP, a Human-in-the-Loop for Prognosis model designed to enhance the reliability and interpretability of prognostic models in clinical contexts, especially when faced with the complexities of missing covariates and outcomes.
MedMerge: Merging Models for Effective Transfer Learning to Medical Imaging Tasks
In this work, we propose MedMerge, a method whereby the weights of different models can be merged, and their features can be effectively utilized to boost performance on a new task.
SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast
Predicting the likelihood of survival is of paramount importance for individuals diagnosed with cancer as it provides invaluable information regarding prognosis at an early stage.
CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis
Deep learning (DL) models have been advancing automatic medical image analysis on various modalities, including echocardiography, by offering a comprehensive end-to-end training pipeline.
XReal: Realistic Anatomy and Pathology-Aware X-ray Generation via Controllable Diffusion Model
Large-scale generative models have demonstrated impressive capacity in producing visually compelling images, with increasing applications in medical imaging.
Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks
This study outlines our methodology for segmenting tumors in the context of two distinct tasks from the BraTS 2023 challenge: Adult Glioma and Pediatric Tumors.
ConDiSR: Contrastive Disentanglement and Style Regularization for Single Domain Generalization
We evaluate our method on the complex task of multicenter histopathology image classification, comparing it against state-of-the-art (SOTA) SDG baselines.
Fine-Tuned Large Language Models for Symptom Recognition from Spanish Clinical Text
The accurate recognition of symptoms in clinical reports is significantly important in the fields of healthcare and biomedical natural language processing.
Multi-Task Learning Approach for Unified Biometric Estimation from Fetal Ultrasound Anomaly Scans
Precise estimation of fetal biometry parameters from ultrasound images is vital for evaluating fetal growth, monitoring health, and identifying potential complications reliably.
FUSC: Fetal Ultrasound Semantic Clustering of Second Trimester Scans Using Deep Self-supervised Learning
This study presents an unsupervised approach for automatically clustering ultrasound images into a large range of fetal views, reducing or eliminating the need for manual labeling.
SimLVSeg: Simplifying Left Ventricular Segmentation in 2D+Time Echocardiograms with Self- and Weakly-Supervised Learning
From calculating biomarkers such as ejection fraction to the probability of a patient's heart failure, accurate segmentation of the heart structures allows doctors to assess the heart's condition and devise treatments with greater precision and accuracy.
Ranked #1 on
LV Segmentation
on Echonet-Dynamic
DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy Classification
Many domain generalization techniques were unsuccessful in learning domain-invariant representations due to the large domain shift.
PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism Diagnosis
Previous deep learning efforts have focused on improving the performance of Pulmonary Embolism(PE) diagnosis from Computed Tomography (CT) scans using Convolutional Neural Networks (CNN).
SEDA: Self-Ensembling ViT with Defensive Distillation and Adversarial Training for robust Chest X-rays Classification
Deep Learning methods have recently seen increased adoption in medical imaging applications.
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
LegoNet: Alternating Model Blocks for Medical Image Segmentation
Since the emergence of convolutional neural networks (CNNs), and later vision transformers (ViTs), the common paradigm for model development has always been using a set of identical block types with varying parameters/hyper-parameters.
Prompt-Based Tuning of Transformer Models for Multi-Center Medical Image Segmentation of Head and Neck Cancer
Medical image segmentation is a vital healthcare endeavor requiring precise and efficient models for appropriate diagnosis and treatment.
Surgical tool classification and localization: results and methods from the MICCAI 2022 SurgToolLoc challenge
Unfortunately, obtaining the annotations needed to train machine learning models to identify and localize surgical tools is a difficult task.
Balancing Privacy and Performance for Private Federated Learning Algorithms
Our findings reveal a direct correlation between the optimal number of local steps, communication rounds, and a set of variables, e. g the DP privacy budget and other problem parameters, specifically in the context of strongly convex optimization.
MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models
The number of studies on deep learning for medical diagnosis is expanding, and these systems are often claimed to outperform clinicians.
Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentation
In this work, we propose a weakly supervised machine learning approach that learns from only ceT1 scans and adapts to segment two structures from hrT2 scans: the VS and the cochlea from the crossMoDA dataset.
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
FUSQA: Fetal Ultrasound Segmentation Quality Assessment
In this paper, we propose a simplified Fetal Ultrasound Segmentation Quality Assessment (FUSQA) model to tackle the segmentation quality assessment when no masks exist to compare with.
Learning Confident Classifiers in the Presence of Label Noise
We evaluate the proposed method on a series of classification tasks such as noisy versions of MNIST, CIFAR-10, Fashion-MNIST datasets as well as CIFAR-10N, which is real-world dataset with noisy human annotations.
On the Importance of Image Encoding in Automated Chest X-Ray Report Generation
Chest X-ray is one of the most popular medical imaging modalities due to its accessibility and effectiveness.
A Radiogenomics Pipeline for Lung Nodules Segmentation and Prediction of EGFR Mutation Status from CT Scans
Lung cancer is a leading cause of death worldwide.
How to Train Vision Transformer on Small-scale Datasets?
However, in contrast to convolutional neural networks, Vision Transformer lacks inherent inductive biases.
Self-omics: A Self-supervised Learning Framework for Multi-omics Cancer Data
Lack of annotated data is a significant problem in machine learning, and Self-Supervised Learning (SSL) methods are typically used to deal with limited labelled data.
TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction
We propose TMSS, an end-to-end Transformer based Multimodal network for Segmentation and Survival prediction that leverages the superiority of transformers that lies in their abilities to handle different modalities.
EchoCoTr: Estimation of the Left Ventricular Ejection Fraction from Spatiotemporal Echocardiography
On the other hand, vision transformers can incorporate global details and long sequences but are computationally expensive and typically require more data to train.
Ranked #1 on
on Echonet-Dynamic
Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image Classification
Vision Transformers (ViT) are competing to replace Convolutional Neural Networks (CNN) for various computer vision tasks in medical imaging such as classification and segmentation.
GARDNet: Robust Multi-View Network for Glaucoma Classification in Color Fundus Images
Glaucoma is one of the most severe eye diseases, characterized by rapid progression and leading to irreversible blindness.
Super Images -- A New 2D Perspective on 3D Medical Imaging Analysis
In medical imaging analysis, deep learning has shown promising results.
Color Space-based HoVer-Net for Nuclei Instance Segmentation and Classification
Nuclei segmentation and classification is the first and most crucial step that is utilized for many different microscopy medical analysis applications.
An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data
The main issue when using clinical and imaging data to train a deep learning model is to decide on how to combine the information from these sources.
SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification
In our project, we extend the idea of using a VAE model for low dimensional latent space extraction with the self-supervised learning technique of feature subsetting.
Ranked #1 on
Cancer type classification
on TCGA
Hyperparameter Optimization for COVID-19 Chest X-Ray Classification
Despite the introduction of vaccines, Coronavirus disease (COVID-19) remains a worldwide dilemma, continuously developing new variants such as Delta and the recent Omicron.
Deep Learning-based Quality Assessment of Clinical Protocol Adherence in Fetal Ultrasound Dating Scans
To assess fetal health during pregnancy, doctors use the gestational age (GA) calculation based on the Crown Rump Length (CRL) measurement in order to check for fetal size and growth trajectory.
Automatic Segmentation of Head and Neck Tumor: How Powerful Transformers Are?
Cancer is one of the leading causes of death worldwide, and head and neck (H&N) cancer is amongst the most prevalent types.
Self-Supervision and Multi-Task Learning: Challenges in Fine-Grained COVID-19 Multi-Class Classification from Chest X-rays
Quick and accurate diagnosis is of paramount importance to mitigate the effects of COVID-19 infection, particularly for severe cases.
Is it Possible to Predict MGMT Promoter Methylation from Brain Tumor MRI Scans using Deep Learning Models?
A couple of recent publications proposed a connection between the MGMT promoter state and the MRI scans of the tumor and hence suggested the use of deep learning models for this purpose.
Contrastive Pretraining for Echocardiography Segmentation with Limited Data
Our results show that contrastive pretraining helps improve the performance on left ventricle segmentation, particularly when annotated data is scarce.
