We are looking forward to presenting our work at the AIDP2021 workshop at ICPR 2021. The following two papers were accepted for presentation.
“Classification of noisy free-text prostate cancer pathology reports using natural language processing”, by Anjani Dhrangadhariya, Sebastian Otalora, Manfredo Atzori and Henning Mueller
“Semi-supervised learning with a teacher-student paradigm for histopathology classification: a resource to face data heterogeneity and lack of local annotations”, by Niccolò Marini, Sebastian Otalora, Henning Mueller and Manfredo Atzori.
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/
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 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.
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 byProf. Dr. Roger Gassert, Prof. Dr. Henning Müller, and Dr. Manfredo Atzori.
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.