Author

Víctor M. González

5 papers indexed here

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Preprint Sep 2026

Automatic Generation of Expert-Level Neuron Segmentation Masks from Fluorescence Microscopy Images for Non-Invasive Deep Learning Analysis of Phase-Contrast Images

Background&Objective Accurate segmentation of neurons in microscopy images of neuronal cultures is crucial for research on neurodegenerative diseases and neurotoxicity. Manual annotation of such images is time-consuming, subjective, and inconsistent across experts. Deep learning (DL) models offer an effective alternati...

Gerard Villarroya-Piqué, Víctor M. González, E. Serrano-Pertierra et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Evaluating ADC-only deep learning pipelines for breast cancer detection and segmentation using standalone diffusion-weighted MRI

Dynamic contrast-enhanced (DCE) imaging is the gold standard technique for the detection and characterization of breast cancer using magnetic resonance imaging (MRI). However, DCE-MRI requires long acquisition times and the administration of contrast into the bloodstream, which can cause allergic reactions. Alternative...

Pablo García Marcos, Paula Puerta Gonzĺez, Guillermo Lorenzo et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy Using Temporal Deep Learning on DWI

It is demonstrated that DW-MRI-based deep learning on tumor-centered patches constitutes a minimally invasive, clinically deployable strategy for early pCR prediction, with direct implications for personalized treatment adaptation in neoadjuvant breast cancer therapy.

Pablo García Marcos, M. Islam, Paula Puerta González et al. · 0 citations

#artificial intelligence Preprint Aug 2026

Neoadjuvant chemotherapy response prediction using pretreatment diffusion and contrast-enhanced magnetic resonance imaging with clinical variables

Prediction of pathological complete response before neoadjuvant chemotherapy may facilitate more tailored therapeutic planning for breast cancer patients. This work proposes a deep-learning model for pretreatment data only, combining apparent diffusion coefficient maps, dynamic contrast-enhanced magnetic resonance imag...

Pablo García Marcos, Paula Puerta González, Guillermo Lorenzo et al. · 0 citations

Preprint Aug 2026

NeuroAdaptTrainer: A Fiji/ImageJ Plugin for YOLO-Based Neuron Segmentation, InteractiveCorrection and Transfer Learning

Neuron counting and segmentation in microscopy images of neuronal cultures is a routine and time-consuming task in neuroscience research, traditionally performed through manual inspection or semi-automatic tools. We present NeuroAdaptTrainer, an open-source Fiji/ImageJ plugin that integrates a YOLO instance-segmentatio...

Daniela Eraso-Casas, Gerard Villarroya-Pique, E. Serrano-Pertierra et al. · 0 citations