Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops
ISIC 2023, Care-AI 2023, MedAGI 2023, DeCaF 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings
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Article
Open AccessA reinforcement learning model for AI-based decision support in skin cancer
We investigated whether human preferences hold the potential to improve diagnostic artificial intelligence (AI)-based decision support using skin cancer diagnosis as a use case. We utilized nonuniform rewards ...
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Chapter and Conference Paper
Test-Time Selection for Robust Skin Lesion Analysis
Skin lesion analysis models are biased by artifacts placed during image acquisition, which influence model predictions despite carrying no clinical information. Solutions that address this problem by regulariz...
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Chapter and Conference Paper
An Adversarial Method for Semi-supervised Segmentation of Smoke and Fire in Images
Detecting and segmenting fire and smoke on images and videos is an essential tool for autonomous systems to battle fire incidents. State-of-the-art methods based on Convolutional Neural Networks (CNNs) require...
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Chapter and Conference Paper
Forest Fires Identification Using Self-Supervised Learning
Forest fires are responsible for the destruction of thousands of hectares and infrastructures every year. To minimize their disastrous effects, it is necessary to i) accomplish and early detection and ii) ensu...
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Chapter and Conference Paper
Global and Local Explanations for Skin Cancer Diagnosis Using Prototypes
Providing visual cues to justify the decisions of deep neural networks contributes significantly to increase their explainability. Typical strategies to provide explanations rely on saliency or attention maps ...
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Chapter and Conference Paper
Artifact-Based Domain Generalization of Skin Lesion Models
Deep Learning failure cases are abundant, particularly in the medical area. Recent studies in out-of-distribution generalization have advanced considerably on well-controlled synthetic datasets, but they do no...
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Book and Conference Proceedings
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Chapter and Conference Paper
Diagnosis of Skin Cancer Using Hierarchical Neural Networks and Metadata
Skin cancer cases have been increasing over the years, making it one of the most common cancers. To reduce the high mortality rate, an early and correct diagnosis is necessary. Doctors divide skin lesions into...
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Chapter and Conference Paper
Correction to: Predictive Biomarkers in Melanoma: Detection of BRAF Mutation Using Dermoscopy
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Chapter and Conference Paper
Predictive Biomarkers in Melanoma: Detection of BRAF Mutation Using Dermoscopy
The survival of melanoma patients greatly depends on a timely diagnosis followed by the definition of the most suitable treatment. In the last decade, the number of available therapies for melanoma has increas...
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Chapter and Conference Paper
Improving the Explainability of Skin Cancer Diagnosis Using CBIR
Explainability is a key feature for computer-aided diagnosis systems. This property not only helps doctors understand their decisions, but also allows less experienced practitioners to improve their knowledge....
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Chapter and Conference Paper
What Is the Role of Annotations in the Detection of Dermoscopic Structures?
There has been an increasing demand for computer-aided diagnosis systems to become self-explainable. However, in fields such as dermoscopy image analysis this comes at the cost of asking physicians to annotate...
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Chapter and Conference Paper
Description and Recognition of Activity Patterns Using Sparse Vector Fields
Far-field activities represented as time series or trajectories can be summarized in compact representations of frequent patterns. Popular representations such as clustering or probabilistic modeling of trajec...
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Chapter and Conference Paper
Distributed Estimation of Vector Fields
In many surveillance applications the area of interest is either wide or includes alleys or corners. Thus, the images from multiple cameras need to be combined and this fact motivates the use of distributed op...
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Chapter and Conference Paper
Improving a Switched Vector Field Model for Pedestrian Motion Analysis
Modeling the trajectories of pedestrians is a key task in video surveillance. However, finding a suitable model to describe the trajectories is challenging, mainly because several of the models tend to have a ...
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Chapter and Conference Paper
Local Features Applied to Dermoscopy Images: Bag-of-Features versus Sparse Coding
Feature extraction is a crucial step in any computer aided diagnosis (CAD) system for melanoma diagnosis. Therefore, it is important to select features that are able to efficiently characterize the properties ...
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Chapter and Conference Paper
Color Detection in Dermoscopy Images Based on Scarce Annotations
Dermatologists often prefer clinically oriented Computer Aided Diagnosis (CAD) Systems. However, the development of such systems is not straightforward due to lack of detailed image annotations (medical labels...
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Chapter
A Bag-of-Features Approach for the Classification of Melanomas in Dermoscopy Images: The Role of Color and Texture Descriptors
The identification of melanomas in dermoscopy images is still an up to date challenge. Several Computer Aided-Diagnosis Systems for the early diagnosis of melanomas have been proposed in the last two decades. ...
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Chapter and Conference Paper
What Is the Role of Color Symmetry in the Detection of Melanomas?
Several computer aided diagnosis (CAD) systems have been proposed to detect melanomas in dermoscopy images. Most of them rely on the extraction of several types of visual features: color, texture and shape. Ho...
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Chapter and Conference Paper
Evaluation of Color Based Keypoints and Features for the Classification of Melanomas Using the Bag-of-Features Model
Dermatologists consider color as one of the major discriminative aspects of melanoma. In this paper we evaluate the importance of color in the keypoint detection and description steps of the Bag-of-Features mo...
