Search Results for author:
Found 106 papers, 37 papers with code
Explainable Transformer Prototypes for Medical Diagnoses
Deployments of artificial intelligence in medical diagnostics mandate not just accuracy and efficacy but also trust, emphasizing the need for explainability in machine decisions.
A Probabilistic Hadamard U-Net for MRI Bias Field Correction
Random samples drawn from latent space are then incorporated with a prototypical corrected image to generate multiple plausible images.
Harmonized Spatial and Spectral Learning for Robust and Generalized Medical Image Segmentation
This paper presents a novel approach that synergies spatial and spectral representations to enhance domain-generalized medical image segmentation.
CT Liver Segmentation via PVT-based Encoding and Refined Decoding
Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning.
Ranked #5 on
Liver Segmentation
on LiTS2017
AI Powered Road Network Prediction with Multi-Modal Data
This study presents an innovative approach for automatic road detection with deep learning, by employing fusion strategies for utilizing both lower-resolution satellite imagery and GPS trajectory data, a concept never explored before.
PGS: Pose-Guided Supervision for Mitigating Clothes-Changing in Person Re-Identification
Person Re-Identification (Re-ID) task seeks to enhance the tracking of multiple individuals by surveillance cameras.
Clothes Changing Person Re-Identification
Person Retrieval
+2
Adaptive Smooth Activation for Improved Disease Diagnosis and Organ Segmentation from Radiology Scans
We apply this new activation function to two important and commonly used general tasks in medical image analysis: automatic disease diagnosis and organ segmentation in CT and MRI.
Rethinking Intermediate Layers design in Knowledge Distillation for Kidney and Liver Tumor Segmentation
HLFD strategically distills knowledge from a combination of middle layers to earlier layers and transfers final layer knowledge to intermediate layers at both the feature and pixel levels.
FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation
Semantic segmentation, a crucial task in computer vision, often relies on labor-intensive and costly annotated datasets for training.
HCA-Net: Hierarchical Context Attention Network for Intervertebral Disc Semantic Labeling
In addition, we introduce a skeletal loss term to reinforce the model's geometric dependence on the spine.
Leveraging Unlabeled Data for 3D Medical Image Segmentation through Self-Supervised Contrastive Learning
Current 3D semi-supervised segmentation methods face significant challenges such as limited consideration of contextual information and the inability to generate reliable pseudo-labels for effective unsupervised data use.
INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings
INCODE comprises a harmonizer network and a composer network, where the harmonizer network dynamically adjusts key parameters of the activation function.
SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation
When evaluating skin lesion and brain tumor segmentation datasets, we observe a remarkable improvement of 1. 71% in Intersection-over Union scores for skin lesion segmentation and of 8. 58% for brain tumor segmentation.
EMIT-Diff: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model
We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data that preserve the essential characteristics of the original medical images by incorporating edge information of objects to guide the synthesis process.
A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network
Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children.
Domain Generalization with Fourier Transform and Soft Thresholding
However, it neglects background interference in the amplitude spectrum.
Radiomics Boosts Deep Learning Model for IPMN Classification
We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field.
Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation
To address these challenges, we introduce the concept of \textbf{Deformable Large Kernel Attention (D-LKA Attention)}, a streamlined attention mechanism employing large convolution kernels to fully appreciate volumetric context.
Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection
Vision Transformer (ViT) models have demonstrated a breakthrough in a wide range of computer vision tasks.
Self-supervised Semantic Segmentation: Consistency over Transformation
Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures.
Prototype Learning for Out-of-Distribution Polyp Segmentation
Our model is designed to perform effectively on out-of-distribution (OOD) datasets from multiple centers.
Ensemble Learning with Residual Transformer for Brain Tumor Segmentation
Brain tumor segmentation is an active research area due to the difficulty in delineating highly complex shaped and textured tumors as well as the failure of the commonly used U-Net architectures.
An objective validation of polyp and instrument segmentation methods in colonoscopy through Medico 2020 polyp segmentation and MedAI 2021 transparency challenges
Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps.
GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection
Integrating real-time artificial intelligence (AI) systems in clinical practices faces challenges such as scalability and acceptance.
A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications
Generative Adversarial Networks (GANs) have demonstrated their ability to generate synthetic samples that match a target distribution.
TransRUPNet for Improved Out-of-Distribution Generalization in Polyp Segmentation
We develop a novel real-time deep learning based architecture, TransRUPNet that is based on a Transformer and residual upsampling network for colorectal polyp segmentation to improve OOD generalization.
GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification
To our best knowledge, GazeGNN is the first work that adopts GNN to integrate image and eye-gaze data.
Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification
In this study, our goal is to show the impact of self-supervised pre-training of transformers for organ at risk (OAR) and tumor segmentation as compared to costly fully-supervised learning.
GazeSAM: What You See is What You Segment
This study investigates the potential of eye-tracking technology and the Segment Anything Model (SAM) to design a collaborative human-computer interaction system that automates medical image segmentation.
Vision Transformer for Efficient Chest X-ray and Gastrointestinal Image Classification
Convolutional neural networks (CNNs) have become the de-facto standard in medical image analysis tasks because of their ability to learn complex features from the available datasets, which makes them surpass humans in many image-understanding tasks.
Ensuring Trustworthy Medical Artificial Intelligence through Ethical and Philosophical Principles
Artificial intelligence (AI) methods hold immense potential to revolutionize numerous medical care by enhancing the experience of medical experts and patients.
DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos
Identifying unusual driving behaviors exhibited by drivers during driving is essential for understanding driver behavior and the underlying causes of crashes.
Real-time Multi-Class Helmet Violation Detection Using Few-Shot Data Sampling Technique and YOLOv8
Real-time implementation of such systems is crucial for traffic surveillance and enforcement, however, most of these systems are not real-time.
Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation
In real-world scenarios, however, it is common for models to encounter data from new and different domains to which they were not exposed to during training.
TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testing
Therefore, we intend to develop a novel real-time deep learning based architecture, Transformer based Residual network (TransNetR), for colon polyp segmentation and evaluate its diagnostic performance.
Ranked #1 on
Polyp Segmentation
on PolypGen
Selecting the Best Optimizers for Deep Learning based Medical Image Segmentation
To this end, we proposed a new cyclic optimization method (\textit{CLMR}) to address the efficiency and accuracy problems in deep learning based medical image segmentation.
The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence
We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.
RUPNet: Residual upsampling network for real-time polyp segmentation
Here, we propose a novel architecture, Residual Upsampling Network (RUPNet) for colon polyp segmentation that can process in real-time and show high recall and precision.
Ranked #45 on
Medical Image Segmentation
on Kvasir-SEG
Domain Generalization with Correlated Style Uncertainty
In this regard, style augmentation is a strong DG method taking advantage of instance-specific feature statistics containing informative style characteristics to synthetic novel domains.
A Critical Appraisal of Data Augmentation Methods for Imaging-Based Medical Diagnosis Applications
Current data augmentation techniques and transformations are well suited for improving the size and quality of natural image datasets but are not yet optimized for medical imaging.
Multi-Scale Fusion Methodologies for Head and Neck Tumor Segmentation
Head and Neck (H\&N) organ-at-risk (OAR) and tumor segmentations are essential components of radiation therapy planning.
DilatedSegNet: A Deep Dilated Segmentation Network for Polyp Segmentation
DilatedSegNet is an encoder-decoder network that uses pre-trained ResNet50 as the encoder from which we extract four levels of feature maps.
An Efficient Multi-Scale Fusion Network for 3D Organ at Risk (OAR) Segmentation
Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning.
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions
To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge.
COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers
Monitoring of prevalent airborne diseases such as COVID-19 characteristically involves respiratory assessments.
Multi-Contrast MRI Segmentation Trained on Synthetic Images
Based on synthetic image training, our segmentation results were as high as 93. 91\%, 94. 11\%, 91. 63\%, 95. 33\%, for muscle, fat, bone, and bone marrow delineation, respectively.
Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images
Early detection of precancerous cysts or neoplasms, i. e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more favourable outcome.
TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation
With high efficacy in our performance metrics, we concluded that TransResU-Net could be a strong benchmark for building a real-time polyp detection system for the early diagnosis, treatment, and prevention of colorectal cancer.
Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network
We compare our FocalConvNet with other SOTA on Kvasir-Capsule, a large-scale VCE dataset with 44, 228 frames with 13 classes of different anomalies.
Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network
The detection and removal of precancerous polyps through colonoscopy is the primary technique for the prevention of colorectal cancer worldwide.
Dynamic Linear Transformer for 3D Biomedical Image Segmentation
Transformer-based neural networks have surpassed promising performance on many biomedical image segmentation tasks due to a better global information modeling from the self-attention mechanism.
Transformer based Generative Adversarial Network for Liver Segmentation
The premise behind this choice is that the self-attention mechanism of the Transformers allows the network to aggregate the high dimensional feature and provide global information modeling.
TGANet: Text-guided attention for improved polyp segmentation
Even though there are deep learning methods developed for this task, variability in polyp size can impact model training, thereby limiting it to the size attribute of the majority of samples in the training dataset that may provide sub-optimal results to differently sized polyps.
Out of Distribution Detection, Generalization, and Robustness Triangle with Maximum Probability Theorem
Maximum Probability Framework, powered by Maximum Probability Theorem, is a recent theoretical development in artificial intelligence, aiming to formally define probabilistic models, guiding development of objective functions, and regularization of probabilistic models.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Enhancing Organ at Risk Segmentation with Improved Deep Neural Networks
Organ at risk (OAR) segmentation is a crucial step for treatment planning and outcome determination in radiotherapy treatments of cancer patients.
Hierarchical 3D Feature Learning for Pancreas Segmentation
We propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans.
Deformable Capsules for Object Detection
In this study, we introduce a new family of capsule networks, deformable capsules (DeformCaps), to address a very important problem in computer vision: object detection.
Information Bottleneck Attribution for Visual Explanations of Diagnosis and Prognosis
We provide an innovative visual explanation algorithm for general purpose and as an example application, we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels.
Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data
Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory.
Variational Capsule Encoder
We propose a novel capsule network based variational encoder architecture, called Bayesian capsules (B-Caps), to modulate the mean and standard deviation of the sampling distribution in the latent space.
Brain Tumor Survival Prediction using Radiomics Features
Thus, accurate survival prognosis is an important step in treatment planning.
Adipose Tissue Segmentation in Unlabeled Abdomen MRI using Cross Modality Domain Adaptation
Abdominal fat quantification is critical since multiple vital organs are located within this region.
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.
Capsules for Biomedical Image Segmentation
The proposed convolutional-deconvolutional capsule network, SegCaps, shows state-of-the-art results while using a fraction of the parameters of popular segmentation networks.
Deep Learning for Musculoskeletal Image Analysis
The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and ultrasound) and their precise analysis by expert radiologists.
Hybrid Apparent Diffusion Coefficient (HADC) Map
This work can considerably improve the quality of mpMRI combined assessment for prostate cancer detection.
Diagnosing Colorectal Polyps in the Wild with Capsule Networks
In this study, we design a novel capsule network architecture (D-Caps) to improve the viability of optical biopsy of colorectal polyps.
Maximum Probability Theorem: A Framework for Probabilistic Learning
Instead, the regularizing effects of assuming prior over parameters is seen through maximizing probabilities of models or according to information theory, minimizing the information content of a model.
A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology
Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology.
Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses
To the best of our knowledge, this is the first study to investigate capsule networks for making predictions based on radiologist-level interpretable attributes and its applications to medical image diagnosis.
Cross-modality Knowledge Transfer for Prostate Segmentation from CT Scans
By using SynCT images (without segmentation labels) and MR images (with segmentation labels available), we have trained a deep segmentation network for precise delineation of prostate from real CT scans.
Weakly Supervised Segmentation by A Deep Geodesic Prior
To alleviate this limitation, in this study, we propose a weakly supervised image segmentation method based on a deep geodesic prior.
Electroencephalography based Classification of Long-term Stress using Psychological Labeling
Stress research is a rapidly emerging area in thefield of electroencephalography (EEG) based signal processing. The use of EEG as an objective measure for cost effective andpersonalized stress management becomes important in particularsituations such as the non-availability of mental health facilities. In this study, long-term stress is classified using baseline EEGsignal recordings.
INN: Inflated Neural Networks for IPMN Diagnosis
This is one of the first studies to train an end-to-end deep network on multisequence MRI for IPMN diagnosis, and shows that our proposed novel inflated network architectures are able to handle the extremely limited training data (139 MRI scans), while providing an absolute improvement of $8. 76\%$ in accuracy for diagnosing IPMN over the current state-of-the-art.
PAN: Projective Adversarial Network for Medical Image Segmentation
Adversarial learning has been proven to be effective for capturing long-range and high-level label consistencies in semantic segmentation.
Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals
The response to this enhanced multimedia content (mulsemedia) is evaluated in terms of the appreciation/emotion by using human brain signals.
Classification of Perceived Human Stress using Physiological Signals
In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals.
Relational Reasoning Network (RRN) for Anatomical Landmarking
Purpose: We perform anatomical landmarking for craniomaxillofacial (CMF) bones without explicitly segmenting them.
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge
This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017.
Instance-Level Microtubule Tracking
Our experimental results show that the proposed supervised learning algorithm improves the precision for MT instance velocity estimation drastically to 71. 3% from the baseline result (29. 3%).
Artificial Intelligence Assisted Infrastructure Assessment Using Mixed Reality Systems
Conventional methods for visual assessment of civil infrastructures have certain limitations, such as subjectivity of the collected data, long inspection time, and high cost of labor.
A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation
Magnetic resonance imaging (MRI) is the non-invasive modality of choice for body tissue composition analysis due to its excellent soft tissue contrast and lack of ionizing radiation.
Deep Geodesic Learning for Segmentation and Anatomical Landmarking
Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identification of 9 anatomical landmarks of the mandible on the geodesic space.
Automatically Designing CNN Architectures for Medical Image Segmentation
Deep neural network architectures have traditionally been designed and explored with human expertise in a long-lasting trial-and-error process.
End to End Brain Fiber Orientation Estimation using Deep Learning
Understanding the structure and organization of the tissues facilitates us with a diagnosis method to identify any aberrations and provide acute information on the occurrences of brain ischemia or stroke, the mutation of neurological diseases such as Alzheimer, multiple sclerosis and so on.
S4ND: Single-Shot Single-Scale Lung Nodule Detection
Our approach uses a single feed forward pass of a single network for detection and provides better performance when compared to the current literature.
Capsules for Object Segmentation
A new architecture recently introduced by Sabour et al., referred to as a capsule networks with dynamic routing, has shown great initial results for digit recognition and small image classification.
A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning
Computer aided diagnostic (CAD) tools are developed to help radiologists to compensate for some of these errors.
Semi-supervised multi-task learning for lung cancer diagnosis
This study set out to test the hypothesis that joint learning of false positive (FP) nodule reduction and nodule segmentation can improve the computer aided diagnosis (CAD) systems' performance on both tasks.
Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches
We evaluate our proposed supervised and unsupervised learning algorithms on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans, respectively, and obtain the state-of-the-art sensitivity and specificity results in both problems.
How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosis
To address this challenge, radiologists need computer aided diagnosis (CAD) systems which can assist in learning discriminative imaging features corresponding to malignant and benign nodules.
Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis
Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically identifiable precursors to pancreatic cancer; hence, early detection and precise risk assessment of IPMN are vital.
Simultaneous Detection and Quantification of Retinal Fluid with Deep Learning
We propose a new deep learning approach for automatic detection and segmentation of fluid within retinal OCT images.
Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT
These measurements are derived as outcomes of precise segmentation of the heart and its substructures.
Automatic Response Assessment in Regions of Language Cortex in Epilepsy Patients Using ECoG-based Functional Mapping and Machine Learning
In this study, we address the accuracy limitation of the current RTFM signal estimation methods by analyzing the full frequency spectrum of the signal and replacing signal power estimation methods with machine learning algorithms, specifically random forest (RF), as a proof of concept.
CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-View CNN
Anatomical and biophysical modeling of left atrium (LA) and proximal pulmonary veins (PPVs) is important for clinical management of several cardiac diseases.
Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning
In order to address the need for a large amount for training data for CNN, we resort to transfer learning to obtain highly discriminative features.
TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process
Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning.
Robust and fully automated segmentation of mandible from CT scans
Mandible bone segmentation from computed tomography (CT) scans is challenging due to mandible's structural irregularities, complex shape patterns, and lack of contrast in joints.
Characterization of Lung Nodule Malignancy using Hybrid Shape and Appearance Features
Both sets of features were combined to estimate the nodule malignancy using a random forest classifier.
Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation
During diagnostic assessment of lung CT scans, the radiologists' gaze information were used to create a visual attention map.
Context Driven Label Fusion for segmentation of Subcutaneous and Visceral Fat in CT Volumes
Quantification of adipose tissue (fat) from computed tomography (CT) scans is conducted mostly through manual or semi-automated image segmentation algorithms with limited efficacy.
Optimally Stabilized PET Image Denoising Using Trilateral Filtering
Low-resolution and signal-dependent noise distribution in positron emission tomography (PET) images makes denoising process an inevitable step prior to qualitative and quantitative image analysis tasks.
Near-optimal Keypoint Sampling for Fast Pathological Lung Segmentation
In this paper, we present a novel approach for fast, accurate, reliable segmentation of pathological lungs from CT scans by combining region-based segmentation method with local descriptor classification that is performed on an optimized sampling grid.
CIDI-Lung-Seg: A Single-Click Annotation Tool for Automatic Delineation of Lungs from CT Scans
Accurate and fast extraction of lung volumes from computed tomography (CT) scans remains in a great demand in the clinical environment because the available methods fail to provide a generic solution due to wide anatomical variations of lungs and existence of pathologies.
Parallel AdaBoost Algorithm for Gabor Wavelet Selection in Face Recognition
In this paper, the problem of automatic Gabor wavelet selection for face recognition is tackled by introducing an automatic algorithm based on Parallel AdaBoosting method.
