Search Results for author:
Found 15 papers, 7 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.
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.
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.
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.
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.
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).
FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack Detection
In FedSIS, a hybrid Vision Transformer (ViT) architecture is learned using a combination of FL and split learning to achieve robustness against statistical heterogeneity in the client data distributions without any sharing of raw data (thereby preserving privacy).
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.
FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling
In this work, we propose a framework for medical imaging classification tasks called Federated Split learning of Vision transformer with Block Sampling (FeSViBS).
Arabic Dysarthric Speech Recognition Using Adversarial and Signal-Based Augmentation
We perform further validation on real English dysarthric speech showing a WER improvement of 124% compared to the baseline trained only on healthy English LJSpeech dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
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.
Self-Supervised Transformers for Activity Classification using Ambient Sensors
This paper proposes a methodology based on Transformer Neural Networks to classify the activities of a resident within an ambient sensor based environment.
Generative Adversarial Stacked Autoencoders
Generative Adversarial Networks (GANs) have become predominant in image generation tasks.
