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1.
He, H. et al.: Machine learning analysis of human skin by optoacoustic mesoscopy for automated extraction of psoriasis and aging biomarkers. IEEE Trans. Med. Imaging 43, 2074-2085 (2024)
2.
Kreitner, L.* et al.: Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations. IEEE Trans. Med. Imaging 43, 2061-2073 (2024)
3.
Lagogiannis, I.* ; Meissen, F.* ; Kaissis, G. & Rueckert, D.*: Unsupervised pathology detection: A deep dive Into the state of the art. IEEE Trans. Med. Imaging 43, 241-252 (2024)
4.
Martinez-Sanchez, A.* ; Lamm, L. ; Jasnin, M. & Phelippeau, H.*: Simulating the cellular context in synthetic datasets for cryo-electron tomography. IEEE Trans. Med. Imaging, DOI: 10.1109/TMI.2024.3398401 (2024)
5.
Müller, P.* ; Meissen, F.* ; Kaissis, G. & Rueckert, D.*: Weakly supervised object detection in chest X-rays with differentiable ROI proposal networks and soft ROI pooling. IEEE Trans. Med. Imaging, DOI: 10.1109/TMI.2024.3435015 (2024)
6.
Tenditnaya, A. et al.: Performance assessment and quality control of fluorescence molecular endoscopy with a multi-parametric rigid standard. IEEE Trans. Med. Imaging, DOI: 10.1109/TMI.2024.3398816 (2024)
7.
Spieker, V. et al.: Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review. IEEE Trans. Med. Imaging, DOI: 10.1109/TMI.2023.3323215 (2023)
8.
Tomczak, A.* et al.: Digital staining of white blood cells with confidence estimation. IEEE Trans. Med. Imaging 42, 3895-3906 (2023)
9.
Dehner, C. ; Olefir, I. ; Basak, K. ; Jüstel, D. & Ntziachristos, V.: Deep-learning-based electrical noise removal enables high spectral optoacoustic contrast in deep tissue. IEEE Trans. Med. Imaging 41, 3182-3193 (2022)
10.
Longo, A. ; Jüstel, D. & Ntziachristos, V.: Disentangling the frequency content in optoacoustics. IEEE Trans. Med. Imaging 41, 3373-3384 (2022)
11.
Yu, Z.* et al.: MouseGAN++: Unsupervised disentanglement and contrastive representation for multiple MRI modalities synthesis and structural segmentation of mouse brain. IEEE Trans. Med. Imaging 42, 1197-1209 (2022)
12.
Mustafa, Q. et al.: In vivo three-dimensional Raster Scan Optoacoustic Mesoscopy using Frequency Domain Inversion. IEEE Trans. Med. Imaging 40, 3349-3357 (2021)
13.
Chowdhury, S.P. ; Prakash, J. ; Karlas, A. ; Jüstel, D. & Ntziachristos, V.: A synthetic total impulse response characterization method for correction of hand-held optoacoustic images. IEEE Trans. Med. Imaging 39, 3218-3230 (2020)
14.
Ding, L ; Razansky, D.* & Deán-Ben, X.L.*: Model-based reconstruction of large three-dimensional optoacoustic datasets. IEEE Trans. Med. Imaging 39, 2931-2940 (2020)
15.
Knauer, N. ; Deán-Ben, X.L.* & Razansky, D.*: Spatial compounding of volumetric data enables freehand optoacoustic angiography of large-scale vascular networks. IEEE Trans. Med. Imaging 39, 1160-1169 (2020)
16.
Nitkunanantharajah, S. et al.: Skin surface detection in 3D optoacoustic mesoscopy based on dynamic programming, IEEE Trans. Med. Imaging 39, 458-467 (2020)
17.
Olefir, I. et al.: Deep learning-based spectral unmixing for optoacoustic imaging of tissue oxygen saturation. IEEE Trans. Med. Imaging 39, 3643-3654 (2020)
18.
Özbek, A. ; Dean-Ben, X.L. & Razansky, D.: Compressed optoacoustic sensing of volumetric cardiac motion. IEEE Trans. Med. Imaging 39, 3250-3255 (2020)
19.
Tomczak, A.* et al.: Multi-task multi-domain learning for digital staining and classification of leukocytes. IEEE Trans. Med. Imaging 40, Special Issue on Annotation-efficient Deep Learning for Medical Imaging, 2897-2910 (2020)
20.
Aguirre Bueno, J. et al.: Motion quantification and automated correction in clinical RSOM. IEEE Trans. Med. Imaging 38, 1340-1346 (2019)
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