Search Results for author:
Found 11 papers, 1 papers with code
Transformer-based segmentation of adnexal lesions and ovarian implants in CT images
Two self-supervised pretrained transformer-based segmentation models (SMIT and Swin UNETR) fine-tuned on a dataset of ovarian cancer CT images provided reasonably accurate delineations of the tumors in an independent test dataset.
Self-supervised learning improves robustness of deep learning lung tumor segmentation to CT imaging differences
Self-pretraining is a SSL approach that uses the curated task dataset for both pretraining the networks and fine-tuning them.
Swin transformers are robust to distribution and concept drift in endoscopy-based longitudinal rectal cancer assessment
Endoscopic images are used at various stages of rectal cancer treatment starting from cancer screening, diagnosis, during treatment to assess response and toxicity from treatments such as colitis, and at follow up to detect new tumor or local regrowth (LR).
Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets
All models were fine-tuned on identical datasets for lung tumor segmentation from computed tomography (CT) scans.
Self-distilled Masked Attention guided masked image modeling with noise Regularized Teacher (SMART) for medical image analysis
We also analyzed the impact of semantic attention and noisy teacher on pretraining and downstream accuracy.
Semantic Segmentation with Active Semi-Supervised Representation Learning
We achieve more than 95% of the network's performance on CamVid and CityScapes datasets, utilizing only 12. 1% and 15. 1% of the labeled data, respectively.
Semantic Segmentation with Active Semi-Supervised Learning
Instead, our active learning approach aims to minimize the number of annotations per-image.
AeroRIT: A New Scene for Hyperspectral Image Analysis
We investigate applying convolutional neural network (CNN) architecture to facilitate aerial hyperspectral scene understanding and present a new hyperspectral dataset-AeroRIT-that is large enough for CNN training.
Aerial Spectral Super-Resolution using Conditional Adversarial Networks
In contrast to the spectra of ground based images, aerial spectral images have low spatial resolution and suffer from higher noise interference.
Tracking in Aerial Hyperspectral Videos using Deep Kernelized Correlation Filters
Hyperspectral imaging holds enormous potential to improve the state-of-the-art in aerial vehicle tracking with low spatial and temporal resolutions.
Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps
Hyperspectral cameras can provide unique spectral signatures for consistently distinguishing materials that can be used to solve surveillance tasks.
