Search Results for author:
Found 29 papers, 22 papers with code
Even Small Correlation and Diversity Shifts Pose Dataset-Bias Issues
Our protocol reveals three findings: 1) Models learn and propagate correlation shifts even with low-bias training; this poses a risk of accumulating and combining unaccountable weak biases; 2) Models learn robust features in high- and low-bias scenarios but use spurious ones if test samples have them; this suggests that spurious correlations do not impair the learning of robust features; 3) Diversity shift can reduce the reliance on spurious correlations; this is counter intuitive since we expect biased models to depend more on biases when invariant features are missing.
Artifact-Based Domain Generalization of Skin Lesion Models
We propose a pipeline that relies on artifacts annotation to enable generalization evaluation and debiasing for the challenging skin lesion analysis context.
A Survey on Deep Learning for Skin Lesion Segmentation
We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance).
Seeing without Looking: Analysis Pipeline for Child Sexual Abuse Datasets
The online sharing and viewing of Child Sexual Abuse Material (CSAM) are growing fast, such that human experts can no longer handle the manual inspection.
Weakly Supervised Attention-based Models Using Activation Maps for Citrus Mite and Insect Pest Classification
Despite the integrated pest mechanical aspect, only a few works on automatic classification have handled images with orange mite characteristics, which means tiny and noisy regions of interest.
CIDEr-R: Robust Consensus-based Image Description Evaluation
This paper shows that CIDEr-D, a traditional evaluation metric for image description, does not work properly on datasets where the number of words in the sentence is significantly greater than those in the MS COCO Captions dataset.
An Evaluation of Self-Supervised Pre-Training for Skin-Lesion Analysis
Self-supervised pre-training appears as an advantageous alternative to supervised pre-trained for transfer learning.
GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review
Despite the growing availability of high-quality public datasets, the lack of training samples is still one of the main challenges of deep-learning for skin lesion analysis.
#PraCegoVer: A Large Dataset for Image Captioning in Portuguese
Thus, inspired by this movement, we have proposed the #PraCegoVer, a multi-modal dataset with Portuguese captions based on posts from Instagram.
Less is More: Sample Selection and Label Conditioning Improve Skin Lesion Segmentation
Segmenting skin lesions images is relevant both for itself and for assisting in lesion classification, but suffers from the challenge in obtaining annotated data.
Debiasing Skin Lesion Datasets and Models? Not So Fast
Data-driven models are now deployed in a plethora of real-world applications - including automated diagnosis - but models learned from data risk learning biases from that same data.
Weakly Supervised Learning Guided by Activation Mapping Applied to a Novel Citrus Pest Benchmark
Pests and diseases are relevant factors for production losses in agriculture and, therefore, promote a huge investment in the prevention and detection of its causative agents.
The Six Fronts of the Generative Adversarial Networks
Generative Adversarial Networks fostered a newfound interest in generative models, resulting in a swelling wave of new works that new-coming researchers may find formidable to surf.
Grape detection, segmentation and tracking using deep neural networks and three-dimensional association
In a test set containing 408 grape clusters from images taken on a trellis-system based vineyard, we have reached an F 1 -score up to 0. 91 for instance segmentation, a fine separation of each cluster from other structures in the image that allows a more accurate assessment of fruit size and shape.
Handling Inter-Annotator Agreement for Automated Skin Lesion Segmentation
We also evaluate how conditioning the ground truths using different (but very simple) algorithms may help to enhance agreement and may be appropriate for some use cases.
Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification
We evaluate that claim for melanoma classification, over 9 CNNs architectures, in 5 sets of splits created on the ISIC Challenge 2017 dataset, and 3 repeated measures, resulting in 135 models.
Combating the Elsagate phenomenon: Deep learning architectures for disturbing cartoons
As the first to explore disturbing content in cartoons, we proceed from the most recent pornography detection literature applying deep convolutional neural networks combined with static and motion information of the video.
(De)Constructing Bias on Skin Lesion Datasets
We fed models with additional clinically meaningful information, which failed to improve the results even slightly, suggesting the destruction of cogent correlations.
Skin Lesion Synthesis with Generative Adversarial Networks
Skin cancer is by far the most common type of cancer.
Data Augmentation for Skin Lesion Analysis
In this work, we investigate the impact of 13 data augmentation scenarios for melanoma classification trained on three CNNs (Inception-v4, ResNet, and DenseNet).
Deep-Learning Ensembles for Skin-Lesion Segmentation, Analysis, Classification: RECOD Titans at ISIC Challenge 2018
This extended abstract describes the participation of RECOD Titans in parts 1 to 3 of the ISIC Challenge 2018 "Skin Lesion Analysis Towards Melanoma Detection" (MICCAI 2018).
Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysis
We use the exhaustive trials to simulate sequential decisions and ensembles, with and without the use of privileged information from the test set.
Knowledge Transfer for Melanoma Screening with Deep Learning
Knowledge transfer impacts the performance of deep learning -- the state of the art for image classification tasks, including automated melanoma screening.
RECOD Titans at ISIC Challenge 2017
This extended abstract describes the participation of RECOD Titans in parts 1 and 3 of the ISIC Challenge 2017 "Skin Lesion Analysis Towards Melanoma Detection" (ISBI 2017).
Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes
Deep learning is the current bet for image classification.
A Mid-level Video Representation based on Binary Descriptors: A Case Study for Pornography Detection
Although these approaches provide good results, they generally have the disadvantage of a high false positive rate since not all images with large areas of skin exposure are necessarily pornographic images, such as people wearing swimsuits or images related to sports.
Deep Neural Networks Under Stress
In recent years, deep architectures have been used for transfer learning with state-of-the-art performance in many datasets.
Towards Automated Melanoma Screening: Proper Computer Vision & Reliable Results
In this paper we survey, analyze and criticize current art on automated melanoma screening, reimplementing a baseline technique, and proposing two novel ones.
Semantic Diversity versus Visual Diversity in Visual Dictionaries
Results for image classification show that as visual dictionaries are based on low-level visual appearances, visual diversity is more important than semantic diversity.
