55 citations · 155 across the 13 of their papers we have counts for
13 papers
An Image is Worth Topics: A Visual Structural Topic Model with Pretrained Image Embeddings
Matías Piqueras, Alexandra Segerberg, Matteo Magnani +2
Political scientists are increasingly interested in analyzing visual content at scale. However, the existing computational toolbox is still in need of methods and models attuned to…
Isolated Channel Vision Transformers: From Single-Channel Pretraining to Multi-Channel Finetuning
Wenyi Lian, Patrick Micke, Joakim Lindblad +1
Vision Transformers (ViTs) have achieved remarkable success in standard RGB image processing tasks. However, applying ViTs to multi-channel imaging (MCI) data, e.g., for medical an…
Roadmap on Deep Learning for Microscopy
Giovanni Volpe, Carolina Wählby, Lei Tian +72
Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasin…
Can representation learning for multimodal image registration be improved by supervision of intermediate layers?
Elisabeth Wetzer, Joakim Lindblad, Nataša Sladoje
Multimodal imaging and correlative analysis typically require image alignment. Contrastive learning can generate representations of multimodal images, reducing the challenging task…
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…
Cross-Modality Sub-Image Retrieval using Contrastive Multimodal Image Representations
Eva Breznik, Elisabeth Wetzer, Joakim Lindblad +1
In tissue characterization and cancer diagnostics, multimodal imaging has emerged as a powerful technique. Thanks to computational advances, large datasets can be exploited to disc…
Cross-Sim-NGF: FFT-Based Global Rigid Multimodal Alignment of Image Volumes using Normalized Gradient Fields
Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje
Multimodal image alignment involves finding spatial correspondences between volumes varying in appearance and structure. Automated alignment methods are often based on local optimi…
Fast computation of mutual information in the frequency domain with applications to global multimodal image alignment
Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje
Multimodal image alignment is the process of finding spatial correspondences between images formed by different imaging techniques or under different conditions, to facilitate hete…
Is Image-to-Image Translation the Panacea for Multimodal Image Registration? A Comparative Study
Jiahao Lu, Johan Öfverstedt, Joakim Lindblad +1
Despite current advancement in the field of biomedical image processing, propelled by the deep learning revolution, multimodal image registration, due to its several challenges, is…
INSPIRE: Intensity and spatial information-based deformable image registration
Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje
We present INSPIRE, a top-performing general-purpose method for deformable image registration. INSPIRE brings distance measures which combine intensity and spatial information into…
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…