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20162024
most citedTowards Automated Melanoma Screening: Exploring Transfer Learning Schemes

34 citations · 34 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.CV2024

FairPIVARA: Reducing and Assessing Biases in CLIP-Based Multimodal Models

Diego A. B. Moreira, Alef Iury Ferreira, Jhessica Silva +10

Despite significant advancements and pervasive use of vision-language models, a paucity of studies has addressed their ethical implications. These models typically require extensiv…

cs.CV2023

The Performance of Transferability Metrics does not Translate to Medical Tasks

Levy Chaves, Alceu Bissoto, Eduardo Valle +1

Transfer learning boosts the performance of medical image analysis by enabling deep learning (DL) on small datasets through the knowledge acquired from large ones. As the number of…

cs.CV2023

Test-Time Selection for Robust Skin Lesion Analysis

Alceu Bissoto, Catarina Barata, Eduardo Valle +1

Skin lesion analysis models are biased by artifacts placed during image acquisition, which influence model predictions despite carrying no clinical information. Solutions that addr…

cs.CV2022

Artifact-Based Domain Generalization of Skin Lesion Models

Alceu Bissoto, Catarina Barata, Eduardo Valle +1

Deep Learning failure cases are abundant, particularly in the medical area. Recent studies in out-of-distribution generalization have advanced considerably on well-controlled synth…

cs.CV201634 cited

Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes

Afonso Menegola, Michel Fornaciali, Ramon Pires +2

Deep learning is the current bet for image classification. Its greed for huge amounts of annotated data limits its usage in medical imaging context. In this scenario transfer learn…