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Conference Paper
Pina O, Dorca E, Vilaplana V. Cell-DETR: Efficient cell detection and classification in WSIs with transformers. In Medical Imaging with Deep Learning (MIDL 2024). 2024.
Petrone P, Casamitjana A, Falcon C, Artigues M, Operto G, Skouras S, et al.. Characteristic Brain Volumetric Changes in the AD Preclinical Signature. In Alzheimer's Association International Conference. Chicago, USA; 2018.
Sayrol E, Fischi O, Pardàs M. Color Inititialization for Lip Tracking. In International Conference on Augmented, Virtual Environments and 3D Imaging. 2001. pp. 351–354.
Salembier P, Pujol O, Garrido L. Connected operators for sprite creation and layered representation of image sequences. In 9th European Signal Processing Conference, EUSIPCO 1998. Rhodes, Greece; 1998. pp. 2105–2108.  (253.51 KB)
Alcoverro M, López-Méndez A, Pardàs M, Casas J. Connected Operators on 3D data for human body analysis. In 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 2011. pp. 9–14.
Pardàs M, Torres L. Connectivity filters for image sequences. In IMAGE ALGEBRA AND MORPHOLOGICAL IMAGE PROCESSING. SPIE. 1992. pp. 318–329.
Pont-Tuset J, Marqués F. Contour detection using binary partition trees. In IEEE International Conference on Image Processing. 2010. pp. 1609–1612.
Hernandez C, Combalia M, Puig S, Malvehy J, Vilaplana V. Contrastive and attention-based multiple instance learning for the prediction of sentinel lymph node status from histopathologies of primary melanoma tumours. In Cancer Prevention through early detecTion (Caption) Workshop at 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). 2022.
Landabaso J-L, Pardàs M. Cooperative background modelling using multiple cameras towards human detection in smart-room. In 14th European Signal Processing Conference. 2006. pp. 1–5.
Baldrich A, Paugam R, Casas J, Pardàs M, Àgueda A, Parsons R, et al.. Deep learning-based methodology for smoke plume segmentation of wildfire images. In 14th International Symposium on Fire Safety Science (IAFSS2023). Tsukuba, Japan: International Association for Fire Safety Science (IAFSS); 2023.  (146.88 KB)
Baldrich A, Paugam R, Casas J, Pardàs M, Àgueda A, Parsons R, et al.. Deep learning-based methodology for smoke plume segmentation of wildfire images. In 14th International Symposium on Fire Safety Science (IAFSS2023). Tsukuba, Japan: International Association for Fire Safety Science (IAFSS); 2023.  (146.88 KB)
Baldrich A, Paugam R, Casas J, Pardàs M, Àgueda A, Parsons R, et al.. Deep learning-based methodology for smoke plume segmentation of wildfire images. In 14th International Symposium on Fire Safety Science (IAFSS2023). Tsukuba, Japan: International Association for Fire Safety Science (IAFSS); 2023.  (146.88 KB)
Baldrich A, Paugam R, Casas J, Pardàs M, Àgueda A, Parsons R, et al.. Deep learning-based methodology for smoke plume segmentation of wildfire images. In 14th International Symposium on Fire Safety Science (IAFSS2023). Tsukuba, Japan: International Association for Fire Safety Science (IAFSS); 2023.  (146.88 KB)
Palou G, Salembier P. Depth Ordering on Image Sequences Using Motion Occlusions. In IEEE Int. Conf. in Image Processing, ICIP 2012. Orlando, Florida, USA; 2012.  (5.42 MB)
Casamitjana A, Petrone P, Falcon C, Artigues M, Operto G, Cacciaglia R, et al.. Detection of Amyloid Positive Cognitively unimpaired individuals using voxel-based machine learning on structural longitudinal brain MRI. In Alzheimer's Association International Conference. 2019.
Asteriadis S, Nikolaidis N, Pitas I, Pardàs M. Detection of facial characteristics based on edge information. In International Conference on Computer Vision Theory and Applications, VISAPP 2007. 2007. pp. 247–250.
Asteriadis S, Nikolaidis N, Pitas I, Pardàs M. Detection of facial characteristics based on edge information. In International Conference on Computer Vision Theory and Applications, VISAPP 2007. 2007. pp. 247–250.
Bellver M, Maninis K-K, Pont-Tuset J, Torres J, Giró-i-Nieto X, van Gool L. Detection-aided liver lesion segmentation using deep learning. In ML4H: Machine Learning for Health Workshop at NIPS 2017. 2017.  (1 MB)
Combalia M, Pérez-Anker J, García-Herrera A, Alos L, Vilaplana V, Marques F, et al.. Digitally Stained Confocal Microscopy through Deep Learning. In International Conference on Medical Imaging with Deep Learning (MIDL 2019). London; 2019.
Combalia M, Pérez-Anker J, García-Herrera A, Alos L, Vilaplana V, Marques F, et al.. Digitally Stained Confocal Microscopy through Deep Learning. In International Conference on Medical Imaging with Deep Learning (MIDL 2019). London; 2019.
Bonafonte A, Mariño J, Pardàs M. Efficient integration of coarticulation and lexical information .. In INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING. 1992. pp. 45–48.
Pardàs M, Bonafonte A, Landabaso J-L. Emotion recognition based on MPEG-4 facial animation parameters. In IEEE International Conference on Acoustics, Speech, and Signal Processing. 2002. pp. 3624–3627.
Gallego J, Pardàs M. Enhanced bayesian foreground segmentation using brightness and color distortion region-based model for shadow removal. In 2010 IEEE International Conference on Image Processing. 2010. pp. 3449–3452.
Anglada D, Sala J, Marqués F, Salembier P, Pardàs M. Enhancing Ki-67 Cell Segmentation with Dual U-Net Models: A Step Towards Uncertainty-Informed Active Learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. IEEE; 2024.  (838.72 KB)
Bosch I, Salvador J, Perez-Pellitero E, Ruiz-Hidalgo J. An Epipolar-Constrained Prior for Efficient Search in Multi-View Scenarios. In EUSIPCO. Lisbon; 2014.  (3.69 MB)

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