106 citations · 213 across the 42 of their papers we have counts for
8 papers · 1 filter
Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime
Thomas Stegmüller, Christian Abbet, Behzad Bozorgtabar +4
Screening Papanicolaou test samples has proven to be highly effective in reducing cervical cancer-related mortality. However, the lack of trained cytopathologists hinders its wides…
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer
Pushpak Pati, Guillaume Jaume, Zeineb Ayadi +4
The segmentation and automatic identification of histological regions of diagnostic interest offer a valuable aid to pathologists. However, segmentation methods are hampered by the…
Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification
Dwarikanath Mahapatra
The success of deep learning has set new benchmarks for many medical image analysis tasks. However, deep models often fail to generalize in the presence of distribution shifts betw…
Anomaly Detection on X-Rays Using Self-Supervised Aggregation Learning
Behzad Bozorgtabar, Dwarikanath Mahapatra, Guillaume Vray +1
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 traini…
Structure Preserving Stain Normalization of Histopathology Images Using Self-Supervised Semantic Guidance
Dwarikanath Mahapatra, Behzad Bozorgtabar, Jean-Philippe Thiran +1
Although generative adversarial network (GAN) based style transfer is state of the art in histopathology color-stain normalization, they do not explicitly integrate structural info…
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer
Christian Abbet, Inti Zlobec, Behzad Bozorgtabar +1
With the long-term rapid increase in incidences of colorectal cancer (CRC), there is an urgent clinical need to improve risk stratification. The conventional pathology report is us…
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution
Mohammad Saeed Rad, Thomas Yu, Claudiu Musat +3
Super-resolution (SR) has traditionally been based on pairs of high-resolution images (HR) and their low-resolution (LR) counterparts obtained artificially with bicubic downsamplin…
Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation
Dwarikanath Mahapatra, Behzad Bozorgtabar, Jean-Philippe Thiran +1
Medical image segmentation is an important task for computer aided diagnosis. Pixelwise manual annotations of large datasets require high expertise and is time consuming. Conventio…