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 …
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 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
Learning interpretable disentangled representations using adversarial vaes
Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice. It has been recently shown that learning …
Learn to Segment Organs with a Few Bounding Boxes
Semantic segmentation is an import task in the medical field to identify the exact extent and orientation of significant structures like organs and pathology. Deep neural networks …
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 …
Generalizing multistain immunohistochemistry tissue segmentation using one-shot color deconvolution deep neural networks
A key challenge in cancer immunotherapy biomarker research is quantification of pattern changes in microscopic whole slide images of tumour biopsies. Drug development requires a …
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 …
