Search Results for author:
Found 24 papers, 6 papers with code
Mask Enhanced Deeply Supervised Prostate Cancer Detection on B-mode Micro-Ultrasound
We propose a novel Mask Enhanced Deeply-supervised Micro-US network, termed MedMusNet, to automatically and more accurately segment prostate cancer to be used as potential targets for biopsy procedures.
Sound event detection based on auxiliary decoder and maximum probability aggregation for DCASE Challenge 2024 Task 4
The proposed auxiliary decoder operates independently from the main decoder, enhancing performance of the convolutional block during the initial training stages by assigning a different weight strategy between main and auxiliary decoder losses.
ProsDectNet: Bridging the Gap in Prostate Cancer Detection via Transrectal B-mode Ultrasound Imaging
We trained and validated ProsDectNet using a cohort of 289 patients who underwent MRI-TRUS fusion targeted biopsy.
Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center Study
We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions.
ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation
Then, to align the source and target features and tackle the memory issue of the traditional contrastive loss, we propose the centroid-based contrastive learning (CCL) and a centroid norm regularizer (CNR) to optimize the contrastive pairs in both direction and magnitude.
Few-shot Unsupervised Domain Adaptation for Multi-modal Cardiac Image Segmentation
This restricts the development of UDA methods for new domains.
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimisation for Multi-modal Cardiac Image Segmentation
The proposed method is based on adversarial learning and adapts network features between source and target domain in different spaces.
Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification
In this paper, we propose a spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardiac LV morphology, regional-wall thickness (RWT), and additionally detecting the cardiac phase cycle (systole and diastole) for a given 3D Cine-magnetic resonance (MR) image sequence.
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the segmentation of LGE CMR.
A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment.
The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks
In this study, we investigate the impact of various data augmentation algorithms, e. g., oversampling, Gaussian Mixture Models (GMMs) and Generative Adversarial Networks (GANs), on solving the class imbalance problem.
COPD Classification in CT Images Using a 3D Convolutional Neural Network
Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world.
Deep Learning-based Denoising of Mammographic Images using Physics-driven Data Augmentation
In this study, we propose a deep learning method based on Convolutional Neural Networks (CNNs) for mammogram denoising to improve the image quality.
Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation
We first train an encoder-decoder CNN on T2-weighted and balanced-Steady State Free Precession (bSSFP) MR images with pixel-level annotation and fine-tune the same network with a limited number of Late Gadolinium Enhanced-MR (LGE-MR) subjects, to adapt the domain features.
A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT
Automatic segmentation of organs-at-risk (OAR) in computed tomography (CT) is an essential part of planning effective treatment strategies to combat lung and esophageal cancer.
Dilated deeply supervised networks for hippocampus segmentation in MRI
Tissue loss in the hippocampi has been heavily correlated with the progression of Alzheimer's Disease (AD).
A Multi-task Framework for Skin Lesion Detection and Segmentation
This underlines the need for an accurate and automatic approach to skin lesion segmentation.
Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI
We employ a 3D fully convolutional network, with dilated convolutions in the lowest level of the network, and residual connections between encoder blocks to incorporate local and global knowledge.
SkinNet: A Deep Learning Framework for Skin Lesion Segmentation
Early detection and segmentation of skin lesions are crucial for timely diagnosis and treatment, necessary to improve the survival rate of patients.
Classification of breast cancer histology images using transfer learning
Breast cancer is one of the leading causes of mortality in women.
Classification
Classification Of Breast Cancer Histology Images
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Comparative Analysis of Unsupervised Algorithms for Breast MRI Lesion Segmentation
Accurate segmentation of breast lesions is a crucial step in evaluating the characteristics of tumors.
Hyper-Hue and EMAP on Hyperspectral Images for Supervised Layer Decomposition of Old Master Drawings
In this work, we propose an image processing pipeline that operates on hyperspectral images to separate such layers.
Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation
The proposed method achieved an average Dice coefficient of 0. 7808$\pm$0. 1729 and Jaccard index of 0. 6704$\pm$0. 2167.
