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Found 21 papers, 7 papers with code
DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images
In recent years, deep learning (DL) has shown great potential in the field of dermatological image analysis.
Attribution-based XAI Methods in Computer Vision: A Review
The advancements in deep learning-based methods for visual perception tasks have seen astounding growth in the last decade, with widespread adoption in a plethora of application areas from autonomous driving to clinical decision support systems.
CIRCLe: Color Invariant Representation Learning for Unbiased Classification of Skin Lesions
While deep learning based approaches have demonstrated expert-level performance in dermatological diagnosis tasks, they have also been shown to exhibit biases toward certain demographic attributes, particularly skin types (e. g., light versus dark), a fairness concern that must be addressed.
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).
Multi-Sample $ζ$-mixup: Richer, More Realistic Synthetic Samples from a $p$-Series Interpolant
Modern deep learning training procedures rely on model regularization techniques such as data augmentation methods, which generate training samples that increase the diversity of data and richness of label information.
Semantic Sensor Network Ontology based Decision Support System for Forest Fire Management
Fire weather indices are widely used to measure fire danger and are used to issue bushfire warnings.
Sleeping Combinatorial Bandits
An algorithm can select a subset of arms from the \emph{availability set} (sleeping bandits) and receive the corresponding reward along with semi-bandit feedback (combinatorial bandits).
Skin3D: Detection and Longitudinal Tracking of Pigmented Skin Lesions in 3D Total-Body Textured Meshes
Our lesion tracking algorithm achieves an average matching accuracy of 88% on a set of detected corresponding pairs of prominent lesions of subjects imaged in different poses, and an average longitudinal accuracy of 71% when encompassing additional errors due to lesion detection.
A Multi-Arm Bandit Approach To Subset Selection Under Constraints
We show that after a certain number of rounds, $\tau$, \newalgo\ outputs a subset of agents that satisfy the average quality constraint with a high probability.
D-LEMA: Deep Learning Ensembles from Multiple Annotations -- Application to Skin Lesion Segmentation
To this end, we propose an ensemble of Bayesian fully convolutional networks (FCNs) for the segmentation task by considering two major factors in the aggregation of multiple ground truth annotations: (1) handling contradictory annotations in the training data originating from inter-annotator disagreements and (2) improving confidence calibration through the fusion of base models' predictions.
Matthews Correlation Coefficient Loss for Deep Convolutional Networks: Application to Skin Lesion Segmentation
The segmentation of skin lesions is a crucial task in clinical decision support systems for the computer aided diagnosis of skin lesions.
Illumination-based Transformations Improve Skin Lesion Segmentation in Dermoscopic Images
The semantic segmentation of skin lesions is an important and common initial task in the computer aided diagnosis of dermoscopic images.
Designing Truthful Contextual Multi-Armed Bandits based Sponsored Search Auctions
It is in the best interest of the center to select an ad that has a high expected value (i. e., probability of getting a click $\times$ value it derives from a click of the ad).
Artificial Intelligence in Glioma Imaging: Challenges and Advances
We believe that these technical approaches will facilitate the development of a fully-functional AI tool in the clinical care of patients with gliomas.
Signed Input Regularization
To test the effectiveness of the proposed idea and compare it with other competing methods, we design several test scenarios, such as classification performance, uncertainty, out-of-distribution, and robustness analyses.
Deep Semantic Segmentation of Natural and Medical Images: A Review
The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class.
Introduction to Concentration Inequalities
In this report, we aim to exemplify concentration inequalities and provide easy to understand proofs for it.
Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis
Skin lesion segmentation is a vital task in skin cancer diagnosis and further treatment.
Improved Inference via Deep Input Transfer
Although numerous improvements have been made in the field of image segmentation using convolutional neural networks, the majority of these improvements rely on training with larger datasets, model architecture modifications, novel loss functions, and better optimizers.
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations
The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations.
Summarization and Visualization of Large Volumes of Broadcast Video Data
Format detection of news videos plays an important role in news video analysis.
