Search Results for author:
Found 44 papers, 5 papers with code
LifeLonger: A Benchmark for Continual Disease Classification
Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch, while cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge.
Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning
Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases.
Prompt-driven Latent Domain Generalization for Medical Image Classification
To address these challenges, we propose a novel DG framework for medical image classification without relying on domain labels, called Prompt-driven Latent Domain Generalization (PLDG).
TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classification
Due to the high resolution of the WSI and the unavailability of patch-level annotations, WSI classification is usually formulated as a weakly supervised problem, which relies on multiple instance learning (MIL) based on patches of a WSI.
Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data Augmentation
We demonstrate the effectiveness of our model on two tasks: (i) we invite certified radiologists to assess the quality of the generated synthetic images against real and other state-of-the-art generative models, and (ii) data augmentation to improve the performance of disease localisation.
Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network
Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity.
GAN Based Medical Image Registration
Conventional approaches to image registration consist of time consuming iterative methods.
Consensus Based Medical Image Segmentation Using Semi-Supervised Learning And Graph Cuts
A novel approach is proposed that obtains consensus segmentations from experts using graph cuts (GC) and semi supervised learning (SSL).
Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution
We propose an image super resolution(ISR) method using generative adversarial networks (GANs) that takes a low resolution input fundus image and generates a high resolution super resolved (SR) image upto scaling factor of $16$.
Chest X-rays Classification: A Multi-Label and Fine-Grained Problem
In this work we have proposed a novel error function, Multi-label Softmax Loss (MSML), to specifically address the properties of multiple labels and imbalanced data.
Deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in X-ray images
We propose a novel weakly supervised method to localize chest pathologies using class aware deep multiscale feature learning.
Night Time Haze and Glow Removal using Deep Dilated Convolutional Network
The night haze removal is a severely ill-posed problem especially due to the presence of various visible light sources with varying colors and non-uniform illumination.
Progressive Generative Adversarial Networks for Medical Image Super resolution
Our primary contribution is in proposing a multistage model where the output image quality of one stage is progressively improved in the next stage by using a triplet loss function.
Training Data Independent Image Registration With GANs Using Transfer Learning And Segmentation Information
Registration is an important task in automated medical image analysis.
Informative sample generation using class aware generative adversarial networks for classification of chest Xrays
Training robust deep learning (DL) systems for disease detection from medical images is challenging due to limited images covering different disease types and severity.
AMD Severity Prediction And Explainability Using Image Registration And Deep Embedded Clustering
We propose a method to predict severity of age related macular degeneration (AMD) from input optical coherence tomography (OCT) images.
sZoom: A Framework for Automatic Zoom into High Resolution Surveillance Videos
While viewing on a mobile device, a user can manually zoom into this high resolution video to get more detailed view of objects and activities.
Generative Adversarial Networks And Domain Adaptation For Training Data Independent Image Registration
This is achieved by unsupervised domain adaptation in the registration process and allows for easier application to different datasets without extensive retraining. To achieve our objective we train a network that transforms the given input image pair to a latent feature space vector using autoencoders.
Synergic Adversarial Label Learning for Grading Retinal Diseases via Knowledge Distillation and Multi-task Learning
In addition, we conduct additional experiments to show the effectiveness of SALL from the aspects of reliability and interpretability in the context of medical imaging application.
Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation
The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
Registration of Histopathogy Images Using Structural Information From Fine Grained Feature Maps
Registration is an important part of many clinical workflows and factually, including information of structures of interest improves registration performance.
Structure Preserving Stain Normalization of Histopathology Images Using Self-Supervised Semantic Guidance
Although generative adversarial network (GAN) based style transfer is state of the art in histopathology color-stain normalization, they do not explicitly integrate structural information of tissues.
Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning
Deep anomaly detection models using a supervised mode of learning usually work under a closed set assumption and suffer from overfitting to previously seen rare anomalies at training, which hinders their applicability in a real scenario.
Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation
In this paper, we systematically discuss and define the two common types of label noise in medical images - disagreement label noise from inconsistency expert opinions and single-target label noise from wrong diagnosis record.
Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation
We demonstrate the benefits of the proposed approach, termed Interpretability-Driven Sample Selection (IDEAL), in an active learning setup aimed at lung disease classification and histopathology image segmentation.
Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition
For example, there are estimated more than 40 different kinds of retinal diseases with variable morbidity, however with more than 30+ conditions are very rare from the global patient cohorts, which results in a typical long-tailed learning problem for deep learning-based screening models.
Gated Fusion Network for SAO Filter and Inter Frame Prediction in Versatile Video Coding
To achieve higher coding efficiency, Versatile Video Coding (VVC) includes several novel components, but at the expense of increasing decoder computational complexity.
CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis
While medical image segmentation is an important task for computer aided diagnosis, the high expertise requirement for pixelwise manual annotations makes it a challenging and time consuming task.
Multi-scale Deep Learning Architecture for Nucleus Detection in Renal Cell Carcinoma Microscopy Image
Clear cell renal cell carcinoma (ccRCC) is one of the most common forms of intratumoral heterogeneity in the study of renal cancer.
Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation
Segmentation of Prostate Cancer (PCa) tissues from Gleason graded histopathology images is vital for accurate diagnosis.
Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning
Gleason grading from histopathology images is essential for accurate prostate cancer (PCa) diagnosis.
Anomaly Detection in Retinal Images using Multi-Scale Deep Feature Sparse Coding
Convolutional Neural Network models have successfully detected retinal illness from optical coherence tomography (OCT) and fundus images.
Outlier-based Autism Detection using Longitudinal Structural MRI
Among other findings, metrics employed for model training as well as reconstruction loss computation impact detection performance, and the coronal modality is found to best encode structural information for ASD detection.
Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification
Using a simpler architecture, our method matches a state of the art SSL based GZSL method for natural images and outperforms all methods for medical images.
Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification
The success of deep learning has set new benchmarks for many medical image analysis tasks.
Improved Super Resolution of MR Images Using CNNs and Vision Transformers
State of the art magnetic resonance (MR) image super-resolution methods (ISR) using convolutional neural networks (CNNs) leverage limited contextual information due to the limited spatial coverage of CNNs.
Leukocyte Classification using Multimodal Architecture Enhanced by Knowledge Distillation
Our unique contribution is two-fold - 1) We present a first of its kind multimodal WBC dataset for WBC classification; 2) We develop a high performing multimodal architecture which is also efficient and low in complexity at the same time.
Probabilistic Integration of Object Level Annotations in Chest X-ray Classification
This shows how two-stage learning of labels from coarse to fine-grained, in particular with object level annotations, is an effective method for more optimal annotation usage.
Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysis
Apart from enabling transparent medical image labeling and interpretation, we achieve report and image-based labeling comparable to prior methods, including state-of-the-art performance in some cases as evidenced by experiments on the Indiana Chest X-ray dataset
Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision
We introduce a human-in-the-loop framework in the model training process such that users can observe and correct the model's decision logic when confounding behaviors happen.
AMAE: Adaptation of Pre-Trained Masked Autoencoder for Dual-Distribution Anomaly Detection in Chest X-Rays
Inspired by a modern self-supervised vision transformer model trained using partial image inputs to reconstruct missing image regions -- we propose AMAE, a two-stage algorithm for adaptation of the pre-trained masked autoencoder (MAE).
Domain Generalization by Learning from Privileged Medical Imaging Information
This paper demonstrates that by using privileged information to predict the severity of intra-layer retinal fluid in optical coherence tomography scans, the classification accuracy of a deep learning model operating on out-of-distribution data improves from $0. 911$ to $0. 934$.
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.
Envisioning MedCLIP: A Deep Dive into Explainability for Medical Vision-Language Models
Explaining Deep Learning models is becoming increasingly important in the face of daily emerging multimodal models, particularly in safety-critical domains like medical imaging.
