1.1k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Politecnico di MilanoIT15 papers
- Otto-von-Guericke-Universität MagdeburgDE6 papers
- Center for Behavioral Brain SciencesDE5 papers
- German Center for Neurodegenerative DiseasesDE4 papers
- European Bioinformatics InstituteGB3 papers
- Heidelberg UniversityDE3 papers
- Humanitas UniversityIT3 papers
- IRCCS Humanitas Research HospitalIT3 papers
- Leiden UniversityNL3 papers
- University of PisaIT3 papers
- Allen Institute for Cell ScienceUS2 papers
- Broad InstituteUS2 papers
27 papers
Transcending the Annotation Bottleneck: AI-Powered Discovery in Biology and Medicine
Soumick Chatterjee
The dependence on expert annotation has long constituted the primary rate-limiting step in the application of artificial intelligence to biomedicine. While supervised learning drov…
Towards Segmenting the Invisible: An End-to-End Registration and Segmentation Framework for Weakly Supervised Tumour Analysis
Budhaditya Mukhopadhyay, Chirag Mandal, Pavan Tummala +3
Liver tumour ablation presents a significant clinical challenge: whilst tumours are clearly visible on pre-operative MRI, they are often effectively invisible on intra-operative CT…
Towards a Theoretical Understanding to the Generalization of RLHF
Zhaochun Li, Mingyang Yi, Yue Wang +2
Reinforcement Learning from Human Feedback (RLHF) and its variants have emerged as the dominant approaches for aligning Large Language Models with human intent. While empirically e…
Automatic identification of diagnosis from hospital discharge letters via weakly supervised Natural Language Processing
Vittorio Torri, Elisa Barbieri, Anna Cantarutti +2
Identifying patient diagnoses from hospital discharge letters is essential for large-scale cohort selection and epidemiological research, but traditional supervised approaches requ…
Enabling Global Image Data Sharing in the Life Sciences
Peter Bajcsy, Sreenivas Bhattiprolu, Katy Boerner +20
Coordinated collaboration is essential to realize the added value of and infrastructure requirements for global image data sharing in the life sciences. In this White Paper, we tak…
Integrating state-sequence analysis to uncover dynamic drug-utilization patterns to profile heart failure patients
Nicole Fontana, Laura Savaré, Francesca Ieva
Globally, the incidence of heart failure is increasing, and its principal treatment involves drug therapy. However, widespread non-adherence to therapies is prevalent among heart f…
Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics
Alberto Archetti, Francesca Ieva, Matteo Matteucci
Survival analysis is a fundamental tool in medicine, modeling the time until an event of interest occurs in a population. However, in real-world applications, survival data are oft…
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Jianning Li, Zongwei Zhou, Jiancheng Yang +154
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from co…
Causal effect of chemotherapy received dose intensity on survival outcome: a retrospective study in osteosarcoma
Marta Spreafico, Francesca Ieva, Marta Fiocco
This study aims to analyse the effects of reducing Received Dose Intensity (RDI) in chemotherapy treatment for osteosarcoma patients on their survival by using a novel approach. In…
Ensemble Methods for Multi-Organ Segmentation in CT Series
Leonardo Crespi, Paolo Roncaglioni, Damiano Dei +5
In the medical images field, semantic segmentation is one of the most important, yet difficult and time-consuming tasks to be performed by physicians. Thanks to the recent advancem…
Comparing Adversarial and Supervised Learning for Organs at Risk Segmentation in CT images
Leonardo Crespi, Mattia Portanti, Daniele Loiacono
Organ at Risk (OAR) segmentation from CT scans is a key component of the radiotherapy treatment workflow. In recent years, deep learning techniques have shown remarkable potential…
bAIoimage analysis: elevating the rate of scientific discovery -- as a community
Damian Edward Dalle Nogare, Matthew Hartley, Joran Deschamps +2
The future of bioimage analysis is increasingly defined by the development and use of tools that rely on deep learning and artificial intelligence (AI). For this trend to continue…
Clustering Hierarchies via a Semi-Parametric Generalized Linear Mixed Model: a statistical significance-based approach
Alessandra Ragni, Chiara Masci, Francesca Ieva +1
We introduce a novel statistical significance-based approach for clustering hierarchical data using semi-parametric linear mixed-effects models designed for responses with laws in…
Community-developed checklists for publishing images and image analysis
Christopher Schmied, Michael Nelson, Sergiy Avilov +51
Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. H…
Imaging-based representation and stratification of intra-tumor Heterogeneity via tree-edit distance
Lara Cavinato, Matteo Pegoraro, Alessandra Ragni +3
Personalized medicine is the future of medical practice. In oncology, tumor heterogeneity assessment represents a pivotal step for effective treatment planning and prognosis predic…
Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging
Hana Haseljić, Soumick Chatterjee, Robert Frysch +9
Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT…
MICDIR: Multi-scale Inverse-consistent Deformable Image Registration using UNetMSS with Self-Constructing Graph Latent
Soumick Chatterjee, Himanshi Bajaj, Istiyak H. Siddiquee +5
Image registration is the process of bringing different images into a common coordinate system - a technique widely used in various applications of computer vision, such as remote…
Chest X-Rays Image Classification from beta-Variational Autoencoders Latent Features
Leonardo Crespi, Daniele Loiacono, Arturo Chiti
Chest X-Ray (CXR) is one of the most common diagnostic techniques used in everyday clinical practice all around the world. We hereby present a work which intends to investigate and…
Longitudinal Latent Overall Toxicity (LOTox) profiles in osteosarcoma: a new taxonomy based on latent Markov models
Marta Spreafico, Francesca Ieva, Marta Fiocco
Due to the presence of multiple types of adverse events with different levels of severity, the analysis of longitudinal toxicity data is a difficult task in cancer studies. In this…
Social and material vulnerability in the face of seismic hazard: an analysis of the Italian case
Oleksandr Didkovskyi, Giovanni Azzone, Alessandra Menafoglio +1
The assessment of the vulnerability of a community endangered by seismic hazard is of paramount importance for planning a precision policy aimed at the prevention and reduction of…
Learning High-Order Interactions via Targeted Pattern Search
Michela C. Massi, Nicola R. Franco, Francesca Ieva +3
Logistic Regression (LR) is a widely used statistical method in empirical binary classification studies. However, real-life scenarios oftentimes share complexities that prevent fro…
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders
Mangal Prakash, Alexander Krull, Florian Jug
Deep Learning based methods have emerged as the indisputable leaders for virtually all image restoration tasks. Especially in the domain of microscopy images, various content-aware…
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
Soumick Chatterjee, Fatima Saad, Chompunuch Sarasaen +10
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the e…
Evidence of economic segregation from mobility lockdown during COVID-19 epidemic
Giovanni Bonaccorsi, Francesco Pierri, Matteo Cinelli +8
In response to the COVID-19 pandemic, National governments have applied lockdown restrictions to reduce the infection rate. We perform a massive analysis on near real-time Italian…