Search Results for author:
Found 23 papers, 10 papers with code
AI-Assisted Cervical Cancer Screening
Most studies proposing AI models retrospectively use a relatively small number of already collected images from specific devices, digital cameras, or smartphones; the challenges and protocol for quality image acquisition during VIA in resource-constrained camp settings, challenges in getting gold standard, data imbalance, etc.
Investigating the Robustness of Vision Transformers against Label Noise in Medical Image Classification
Label noise in medical image classification datasets significantly hampers the training of supervised deep learning methods, undermining their generalizability.
How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?
In this study, we address three key questions: i) How does label noise impact various medical image classification datasets?
Benchmarking Encoder-Decoder Architectures for Biplanar X-ray to 3D Shape Reconstruction
Our results show that attention-based methods that capture global spatial relationships tend to perform better across all anatomies and datasets; performance on clinically relevant subgroups may be overestimated without disaggregated reporting; ribs are substantially more difficult to reconstruct compared to femur, hip and spine; and the dice score improvement does not always bring a corresponding improvement in the automatic estimation of clinically relevant parameters.
Synthetic Boost: Leveraging Synthetic Data for Enhanced Vision-Language Segmentation in Echocardiography
However, the lack of readily available data in echocardiography hampers the training of VLSMs.
Exploring Transfer Learning in Medical Image Segmentation using Vision-Language Models
Medical image segmentation with deep learning is an important and widely studied topic because segmentation enables quantifying target structure size and shape that can help in disease diagnosis, prognosis, surgery planning, and understanding.
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.
Improving Medical Image Classification in Noisy Labels Using Only Self-supervised Pretraining
In this work, we explore contrastive and pretext task-based self-supervised pretraining to initialize the weights of a deep learning classification model for two medical datasets with self-induced noisy labels -- NCT-CRC-HE-100K tissue histological images and COVID-QU-Ex chest X-ray images.
M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis Tasks
Acquiring properly annotated data is expensive in the medical field as it requires experts, time-consuming protocols, and rigorous validation.
Deep-learning assisted detection and quantification of (oo)cysts of Giardia and Cryptosporidium on smartphone microscopy images
The consumption of microbial-contaminated food and water is responsible for the deaths of millions of people annually.
COVID-19-related Nepali Tweets Classification in a Low Resource Setting
Billions of people across the globe have been using social media platforms in their local languages to voice their opinions about the various topics related to the COVID-19 pandemic.
FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation
Supervised deep learning methods for semantic medical image segmentation are getting increasingly popular in the past few years. However, in resource constrained settings, getting large number of annotated images is very difficult as it mostly requires experts, is expensive and time-consuming. Semi-supervised segmentation can be an attractive solution where a very few labeled images are used along with a large number of unlabeled ones.
Label Geometry Aware Discriminator for Conditional Generative Networks
Multi-domain image-to-image translation with conditional Generative Adversarial Networks (GANs) can generate highly photo realistic images with desired target classes, yet these synthetic images have not always been helpful to improve downstream supervised tasks such as image classification.
Uncertainty Estimation in Deep 2D Echocardiography Segmentation
2D echocardiography is the most common imaging modality for cardiovascular diseases.
Automatic Cobb Angle Detection using Vertebra Detector and Vertebra Corners Regression
Correct evaluation and treatment of Scoliosis require accurate estimation of spinal curvature.
Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers
Manual estimation of fetal Head Circumference (HC) from Ultrasound (US) is a key biometric for monitoring the healthy development of fetuses.
FastReg: Fast Non-Rigid Registration via Accelerated Optimisation on the Manifold of Diffeomorphisms
We present an implementation of a new approach to diffeomorphic non-rigid registration of medical images.
Weakly Supervised Localisation for Fetal Ultrasound Images
This paper addresses the task of detecting and localising fetal anatomical regions in 2D ultrasound images, where only image-level labels are present at training, i. e. without any localisation or segmentation information.
Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network
We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes.
Fast Multiple Landmark Localisation Using a Patch-based Iterative Network
PIN is computationally efficient since the inference stage only selectively samples a small number of patches in an iterative fashion rather than a dense sampling at every location in the volume.
Adapted and Oversegmenting Graphs: Application to Geometric Deep Learning
From the adapted graph, we also propose the computation of a dual graph, which inherits the saliency measure from the adapted graph, and whose edges run along image features, hence producing an oversegmenting graph.
Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry
In this paper, we propose a general Riemannian formulation of the pose estimation problem.
3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images
We extensively evaluate the effectiveness of our approach quantitatively on simulated Magnetic Resonance Imaging (MRI), fetal brain imagery with synthetic motion and further demonstrate qualitative results on real fetal MRI data where our method is integrated into a full reconstruction and motion compensation pipeline.
