Staingan: Stain style transfer for digital histological images
Digitized Histological diagnosis is in increasing demand. However, color variations due to various factors are imposing obstacles to the diagnosis process. The problem of stain …
Self-attention equipped graph convolutions for disease prediction
Multi-modal data comprising imaging (MRI, fMRI, PET, etc.) and non-imaging (clinical test, demographics, etc.) data can be collected together and used for disease prediction. Such …
Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer
Style transfer is a field with growing interest and use cases in deep learning. Recent work has shown Generative Adversarial Networks(GANs) can be used to create realistic images …
Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss
Deep learning techniques are recently being used in fundus image analysis and diabetic retinopathy detection. Microaneurysms are an important indicator of diabetic retinopathy …
Learning-based x-ray image denoising utilizing model-based image simulations
X-ray guidance is an integral part of interventional procedures, but the exposure to ionizing radiation poses a non-negligible threat to patients and clinical staff. Unfortunately, …
sai-gokul-hariharan
Learning Interpretable Features via Adversarially Robust Optimization
Neural networks are proven to be remarkably successful for classification and diagnosis in medical applications. However, the ambiguity in the decision-making process and the …
ashkan-khakzar
Investigation of Focal Loss in Deep Learning Models For Femur Fractures Classification
This paper develops an approach based on deep learning for the classifications of a common critical type of bone fractures, namely proximal femur. The performance of the …
Mayar Lotfy
InceptionGCN: receptive field aware graph convolutional network for disease prediction
Geometric deep learning provides a principled and versatile manner for the integration of imaging and non-imaging modalities in the medical domain. Graph Convolutional Networks …
Graph Convolution Based Attention Model for Personalized Disease Prediction
Clinicians implicitly incorporate the complementarity of multi-modal data for disease diagnosis. Often a varied order of importance for this heterogeneous data is considered for …
Deep autoencoding models for unsupervised anomaly segmentation in brain MR images
Reliably modeling normality and differentiating abnormal appearances from normal cases is a very appealing approach for detecting pathologies in medical images. A plethora of such …
