4 citations · 4 across the 2 of their papers we have counts for
7 papers
End-to-end Multiple Instance Learning with Gradient Accumulation
Axel Andersson, Nadezhda Koriakina, Nataša Sladoje +1
Being able to learn on weakly labeled data, and provide interpretability, are two of the main reasons why attention-based deep multiple instance learning (ABMIL) methods have becom…
Oral cancer detection and interpretation: Deep multiple instance learning versus conventional deep single instance learning
Nadezhda Koriakina, Nataša Sladoje, Vladimir Bašić +1
The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample from the oral cavity. This process is time consuming and more…
CoMIR: Contrastive Multimodal Image Representation for Registration
Nicolas Pielawski, Elisabeth Wetzer, Johan Öfverstedt +4
We propose contrastive coding to learn shared, dense image representations, referred to as CoMIRs (Contrastive Multimodal Image Representations). CoMIRs enable the registration of…
A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images
Jiahao Lu, Nataša Sladoje, Christina Runow Stark +3
Oral cancer incidence is rapidly increasing worldwide. The most important determinant factor in cancer survival is early diagnosis. To facilitate large scale screening, we propose…
Stochastic Distance Transform
Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje
The distance transform (DT) and its many variations are ubiquitous tools for image processing and analysis. In many imaging scenarios, the images of interest are corrupted by noise…
Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification
Tomáš Majtner, Buda Bajić, Sule Yildirim +3
In this report, we are presenting our automated prediction system for disease classification within dermoscopic images. The proposed solution is based on deep learning, where we em…