Content-Based Image Retrieval
Our work on “The importance of feature aggregation in radiomics: a head and neck cancer study“, conducted by Pierre Fontaine, Oscar Acosta, Joël Castelli, Renaud De Crevoisier, Henning Müller and Adrien Depeursinge has been published in Scientific Reports.
We show that visual feature aggregation, often overlooked in radiomics studies, plays an important role in the prediction of disease characteristics.
We are looking forward to presenting our work at the AIDP2021 workshop at ICPR 2021. The following two papers were accepted for presentation.
Our paper “DeepHistReg: Unsupervised Deep Learning Registration Framework for Differently Stained Histology Samples“, by Marek Wodzinski and Henning Müller has been published in Computer Methods & Programs in Biomedicine.
The project IMAGINE (Radiomics for comprehensive patient and disease phenotyping in personalized health), a Swiss-wide initiative to promote image-based personalized medicine, had a dedicated Swiss Personalized Health Network (SPHN) webinar on Oct. 7 2020 to detail the recent progress of the consortium. You can view it under this link:
https://sphn.ch/seminar-training/imagine/
Congratulations to Sebastian Otálora, Niccolò Marini, Henning Müller and Manfredo Atzori who received the “People’s choice award” for their paper “Semi-weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks” at LABELS MICCAI 2020.
Congratulations to Marek Wodzinski and Henning Müller for receiving the best paper award for their work on “Unsupervised Learning-based Nonrigid Registration of High Resolution Histology Images” at MICCAI-MLMI workshop. Their deep learning-based unsupervised nonrigid registration method provides excellent registration results with fast inference time.
The recording “In buone mani” (In good hands) was presented at a scientific show of the Swiss Radio-television in Italian language (RSI). The show described the projects ProHand and MeganePro , targeting the development of robotic hands with advanced technologies, such as 3D scanning, additive manufacturing and machine learning.
Our paper on “Prospects and Challenges of Radiomics by Using Nononcologic Routine Chest CT”, Sebastian Röhrich et al., has been accepted for publication in Radiology: Cardiothoracic Imaging.
The paper is now available online: https://pubs.rsna.org/doi/full/10.1148/ryct.2020190190
Our paper entitled “BIAS: Transparent reporting of biomedical image analysis challenges“, by Lena Maier-Hein et al. has been accepted for publication in Medical Image Analysis.
As part of the Biomedical Image Analysis challengeS (BIAS) initiative, we developed a set of recommendations for the reporting of challenges, including a checklist for authors and reviewers.
Our journal paper entitled “A lung graph model for the radiological assessment of chronic thromboembolic pulmonary hypertension in CT” by O. Jimenez del Toro, Y. Dicente Cid, A. Platon, A-L. Hachulla Lemaire, F. Lador, P-A. Poletti and H. Müller has been accepted for publication in Computers in Biology and Medicine.
The paper is now available online: https://www.sciencedirect.com/science/article/abs/pii/S001048252030295X
Our paper entitled “Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging” by M. Graziani, T. Lompech, H. Müller, A. Depeursinge and V. Andrearczyk has been accepted for presentation at the Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC) at MICCAI 2020.
In this paper, we analyze the invariance to scale in standard pre-trained CNNs and the effects of this invariance in a medical imaging task, where scale often carries crucial information.
Our paper entitled entitled “Variability of Muscle Synergies in Hand Grasps: analysis of intra- and inter-session data”, by Una Pale, Manfredo Atzori, Henning Müller and Alessandro Scano has been accepted for publication in Sensors. The full paper is now available online: https://www.mdpi.com/1424-8220/20/15/4297.
Our paper entitled “Semi-Weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks“, by S. Otálora, N. Marini, M. Atzori and H. Müller has been accepted for publication at the MICCAI workshop LABELS 2020: Large-scale Annotation of Biomedical data and Expert Label Synthesis.
In this paper, we propose a teacher-student approach for training data-hungry models using images automatically annotated by a deep CNN model. Our results in the challenging task of automatic grading of prostate cancer images show that using this approach is significantly better than training the CNN with the small set of ground-truth annotations. It opens the possibility to use vast amounts of non-annotated data.
Congratulations to Matteo Cognolato who successfully defended his PhD thesis entitled “Multimodal data fusion to improve the control of myoelectric prosthetic hands“, supervised by Prof. Dr. Roger Gassert, Prof. Dr. Henning Müller, and Dr. Manfredo Atzori.
Two of our papers have been published in the EBook: Digital Personalized Health and Medicine.
“Automating Quality Control for Structured Standardized Radiology Reports Using Text Analysis”, by Anjani Dhrangadhariya, S. Millius, C. Thouly, B. Rizk, D. Fournier, H. Müller, H. Brat.
Available online: https://pubmed.ncbi.nlm.nih.gov/32570346/
“Machine Learning Assisted Citation Screening for Systematic Reviews” by Anjani Dhrangadhariya, R. Hilfiker, R. Schaer, H. Müller.
Available online: http://ebooks.iospress.nl/publication/54173
Our paper entitled “Multimodal Latent Semantic Alignment for Automated Prostate Tissue Classification and Retrieval” has been accepted for presentation at MICCAI 2020.
In this work, our group collaborated with the MindLAB group (Universidad Nacional de Colombia) to develop an information fusion method for the automatic analysis of prostate cancer images.
More details can be found in the paper.
Our paper on Local Rotation Invariance in 3D CNNs, by Vincent Andrearczyk, Julien Fageot, Valentin Oreiller, Xavier Montet and Adrien Depeursinge has been accepted for publication in Medical Image Analysis.
We introduce the idea of local rotation invariance in 3D convolutional networks using steerable filters, extending our MIDL 2019 paper (best paper award).
Our new paper entitled “Concept attribution: Explaining CNN decisions to physicians” by Mara Graziani, Vincent Andrearczyk, Stephane Marchand-Maillet and Henning Müller has been accepted for publication in Computers in Biology and Medicine.
Feature attribution techniques explain Convolution Neural Networks (CNNs) in terms of the input pixels. The abstraction to higher level impacting factors, however, can be difficult when only a pixel-based analysis is performed. In this paper, we generate explanations in terms of prognostic factors for breast cancer, such as nuclei pleomorphism.
The Image Biomarker Standardisation Initiative (IBSI) is proud to launch chapter 2, with the goal of standardizing computations of imaging filters in radiomics. You can find more details on our website.
