activity
20202026
most citedPerformance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification

6 citations · 7 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CV2026

Simultaneous Dual-View Mammogram Synthesis Using Denoising Diffusion Probabilistic Models

Jorge Alberto Garza-Abdala, Gerardo A. Fumagal-González, Eduardo de Avila-Armenta +5

Breast cancer screening relies heavily on mammography, where the craniocaudal (CC) and mediolateral oblique (MLO) views provide complementary information for diagnosis. However, ma…

cs.CV2026★ 1 cited

Ensemble of radiomics and ConvNeXt for breast cancer diagnosis

Jorge Alberto Garza-Abdala, Gerardo Alejandro Fumagal-González, Beatriz A. Bosques-Palomo +4

Early diagnosis of breast cancer is crucial for improving survival rates. Radiomics and deep learning (DL) have shown significant potential in assisting radiologists with early can…

cs.CV2025

MammoRGB: Dual-View Mammogram Synthesis Using Denoising Diffusion Probabilistic Models

Jorge Alberto Garza-Abdala, Gerardo A. Fumagal-González, Daly Avendano +7

Purpose: This study aims to develop and evaluate a three channel denoising diffusion probabilistic model (DDPM) for synthesizing single breast dual view mammograms and to assess th…

eess.IV2025

Comparison of ConvNeXt and Vision-Language Models for Breast Density Assessment in Screening Mammography

Yusdivia Molina-Román, David Gómez-Ortiz, Ernestina Menasalvas-Ruiz +2

Mammographic breast density classification is essential for cancer risk assessment but remains challenging due to subjective interpretation and inter-observer variability. This stu…

eess.IV2024★ 6 cited

Performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification

Sadam Hussain, Mansoor Ali, Usman Naseem +7

Rising breast cancer (BC) occurrence and mortality are major global concerns for women. Deep learning (DL) has demonstrated superior diagnostic performance in BC classification com…

q-bio.PE2020

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young +93

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international…