2 code implementations • 30 Jul 2018 • Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje
The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradient-based registration framework available as part of the open source Insight Segmentation and Registration Toolkit (ITK).
no code implementations • 15 Aug 2018 • Tomáš Majtner, Buda Bajić, Sule Yildirim, Jon Yngve Hardeberg, Joakim Lindblad, Nataša Sladoje
In this report, we are presenting our automated prediction system for disease classification within dermoscopic images.
2 code implementations • 18 Oct 2018 • Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje
We, thus, define a stochastic distance transform (SDT), which has an adjustable robustness to noise.
2 code implementations • 23 Oct 2019 • Jiahao Lu, Nataša Sladoje, Christina Runow Stark, Eva Darai Ramqvist, Jan-Michaél Hirsch, Joakim Lindblad
The pipeline consists of fully convolutional regression-based nucleus detection, followed by per-cell focus selection, and CNN based classification.
1 code implementation • NeurIPS 2020 • Nicolas Pielawski, Elisabeth Wetzer, Johan Öfverstedt, Jiahao Lu, Carolina Wählby, Joakim Lindblad, Nataša Sladoje
We propose contrastive coding to learn shared, dense image representations, referred to as CoMIRs (Contrastive Multimodal Image Representations).
1 code implementation • 14 Dec 2020 • Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje
INSPIRE exhibits excellent performance on the FIRE dataset, substantially outperforming several domain-specific methods.}
2 code implementations • 30 Mar 2021 • Jiahao Lu, Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje
We compare the performance of four Generative Adversarial Network (GAN)-based I2I translation methods and one contrastive representation learning method, subsequently combined with two representative monomodal registration methods, to judge the effectiveness of modality translation for multimodal image registration.
1 code implementation • 28 Jun 2021 • Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje
We propose an efficient algorithm for computing MI for all discrete displacements (formalized as the cross-mutual information function (CMIF)), which is based on cross-correlation computed in the frequency domain.
1 code implementation • 19 Oct 2021 • Johan Öfverstedt, Joakim Lindblad, Nataša Sladoje
The method is fast; a 3. 4Mvoxel global rigid alignment requires approximately 40 seconds of computation, and the proposed algorithm outperforms a direct algorithm for the same task by more than three orders of magnitude.
1 code implementation • 10 Jan 2022 • Eva Breznik, Elisabeth Wetzer, Joakim Lindblad, Nataša Sladoje
We propose a new application-independent content-based image retrieval (CBIR) system for reverse (sub-)image search across modalities, which combines deep learning to generate representations (embedding the different modalities in a common space) with classical feature extraction and bag-of-words models for efficient and reliable retrieval.
1 code implementation • 3 Feb 2022 • Nadezhda Koriakina, Nataša Sladoje, Vladimir Bašić, Joakim Lindblad
Performance at the bag level on real-world cytological data is similar for both methods, yet the single instance approach performs better on average.
1 code implementation • 8 Mar 2022 • Axel Andersson, Nadezhda Koriakina, Nataša Sladoje, Joakim Lindblad
We conduct experiments on both QMNIST and Imagenette to investigate the performance and training time, and compare with the conventional memory-expensive baseline and a recent sampled-based approach.
no code implementations • 1 Mar 2023 • Elisabeth Wetzer, Joakim Lindblad, Nataša Sladoje
Contrastive learning can generate representations of multimodal images, reducing the challenging task of multimodal image registration to a monomodal one.