Adrià Marcos Morales

Leave No Data Behind: Exploring a new paradigm in oncology with foundation models and large language models

Federica Corso, Aleksandra Zec, Margherita Favali, Marta Ligero et autres

Foundation models (FMs) and large language models (LLMs) are transforming cancer AI by integrating heterogeneous data sources, including medical imaging, electronic health records, and molecular profiles. By learning from large-scale, unstructured, and label-free inputs, these models may support diagnosis, biomarker discovery, prognostic …

it, de, es, gb, us, fr, nl (code pays fourni par la source)

A CT-based deep learning-driven tool for automatic liver tumor detection and delineation in patients with cancer

Maria Balaguer‐Montero, Adrià Marcos Morales, Marta Ligero, Christina Zatse et autres

Liver tumors, whether primary or metastatic, significantly impact the outcomes of patients with cancer. Accurate identification and quantification are crucial for effective patient management, including precise diagnosis, prognosis, and therapy evaluation. We present SALSA (system for automatic liver tumor segmentation and detection), …

es, de (code pays fourni par la source)

Visualizing nanoparticle surface dynamics and instabilities enabled by deep denoising

Peter A. Crozier, Matan Leibovich, Piyush Haluai, Mai Tan et autres

Materials functionalities may be associated with atomic-level structural dynamics occurring on the millisecond timescale. However, the capability of electron microscopy to image structures with high spatial resolution and millisecond temporal resolution is often limited by poor signal-to-noise ratios. With an unsupervised deep …

us (code pays fourni par la source)

Atomic Resolution Observations of Nanoparticle Surface Dynamics and Instabilities Enabled by Artificial Intelligence

Peter A. Crozier, Matan Leibovich, Piyush Haluai, Mai Tan et autres

Nanoparticle surface structural dynamics is believed to play a significant role in regulating functionalities such as diffusion, reactivity, and catalysis but the atomic-level processes are not well understood. Atomic resolution characterization of nanoparticle surface dynamics is challenging since it requires both high …

A Deep Learning-Driven Tool for Automatic Liver Tumor Detection and Delineation in Cancer Patients

Maria Balaguer‐Montero, Adrià Marcos Morales, Marta Ligero, Christina Zatse et autres

es (code pays fourni par la source)

The Impact of Artificial Intelligence on In Situ Electron Microscopy

Peter A. Crozier, Adrià Marcos Morales, Matan Leibovich, Sreyas Mohan et autres

A primary goal of in situ electron microscopy is to follow the atomic-level structural evolution taking place in a material due to an applied stimulus. The in situ field started several years after the invention of the transmission electron microscope (TEM) in …

us (code pays fourni par la source)

Extracting High Spatio-Temporal Information Using Machine Learning from Pt Nanoparticles in CO Gas Environment

Piyush Haluai, Adrià Marcos Morales, Matan Leibovich, Mai Tan et autres

In the field of in situ transmission electron microscopy (TEM), the availability of new direct electron detectors is opening up completely new areas of atomic-level materials characterization. The faster acquisition time offers the possibility of exploring structural dynamics with improved temporal resolution. …

us (code pays fourni par la source)

Detecting and Characterizing the Fluxionality in Pt Nanoparticles

Advait Gilankar, Benjamin K. Miller, Adrià Marcos Morales, Piyush Haluai et autres

Advait Gilankar, Benjamin K Miller, Adrià Marcos Morales, Piyush Haluai, Mai Tan, Joshua L Vincent, Carlos Fernandez-Granda, Peter A Crozier; Detecting and

us (code pays fourni par la source)

Application of Deep Unsupervised Convolutional Neural Networks to Denoise Large Temporally Resolved In Situ TEM Datasets

Adrià Marcos Morales, Advait Gilankar, Ramón Manzorro, Piyush Haluai et autres

Adrià Marcos Morales, Advait Gilankar, Ramon Manzorro, Piyush Haluai, Mai Tan, Joshua L Vincent, Carlos Fernandez-Granda, Peter A Crozier; Application of D

us (code pays fourni par la source)