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Found 29 papers, 16 papers with code
Performance Metrics for Probabilistic Ordinal Classifiers
This paper advocates the use of the RPS for image grading tasks.
Why is the winner the best?
The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning.
Multi-Head Multi-Loss Model Calibration
Delivering meaningful uncertainty estimates is essential for a successful deployment of machine learning models in the clinical practice.
AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge
Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible.
Understanding metric-related pitfalls in image analysis validation
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Class Adaptive Network Calibration
Comprehensive evaluation and multiple comparisons on a variety of benchmarks, including standard and long-tailed image classification, semantic segmentation, and text classification, demonstrate the superiority of the proposed method.
On the Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness
Our findings are surprising: CE-Dice loss combinations that excel in segmenting in-distribution images have a poor performance when dealing with OoD data, which leads us to recommend the adoption of the CE loss for this kind of problems, due to its robustness and ability to generalize to OoD samples.
Calibrating Segmentation Networks with Margin-based Label Smoothing
Following our observations, we propose a simple and flexible generalization based on inequality constraints, which imposes a controllable margin on logit distances.
Test Time Transform Prediction for Open Set Histopathological Image Recognition
In test time, we measure model confidence in predicting this transform, which we expect to be lower for images in the Open Set.
Metrics reloaded: Recommendations for image analysis validation
The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output.
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Polyps are well-known cancer precursors identified by colonoscopy.
FUSeg: The Foot Ulcer Segmentation Challenge
Segmentation of wound boundaries in images is a key component of the care and diagnosis protocol since it is important to estimate the area of the wound and provide quantitative measurement for the treatment.
The Devil is in the Margin: Margin-based Label Smoothing for Network Calibration
Following our observations, we propose a simple and flexible generalization based on inequality constraints, which imposes a controllable margin on logit distances.
A Hierarchical Multi-Task Approach to Gastrointestinal Image Analysis
We also describe in this paper our solution for the challenge task of polyp segmentation in colonoscopies, which was addressed with a pretrained double encoder-decoder network.
Convolutional Nets Versus Vision Transformers for Diabetic Foot Ulcer Classification
This paper compares well-established Convolutional Neural Networks (CNNs) to recently introduced Vision Transformers for the task of Diabetic Foot Ulcer Classification, in the context of the DFUC 2021 Grand-Challenge, in which this work attained the first position.
Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation
Polyps represent an early sign of the development of Colorectal Cancer.
Balanced-MixUp for Highly Imbalanced Medical Image Classification
The resulting two sets of samples are then mixed-up to create a more balanced training distribution from which a neural network can effectively learn without incurring in heavily under-fitting the minority classes.
The hidden label-marginal biases of segmentation losses
In the abundant segmentation literature, there is no clear consensus as to which of these losses is a better choice, with varying performances for each across different benchmarks and applications.
Common Limitations of Image Processing Metrics: A Picture Story
While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation.
Cost-Sensitive Regularization for Diabetic Retinopathy Grading from Eye Fundus Images
Assessing the degree of disease severity in biomedical images is a task similar to standard classification but constrained by an underlying structure in the label space.
The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models
Our analysis demonstrates that the retinal vessel segmentation problem is far from solved when considering test images that differ substantially from the training data, and that this task represents an ideal scenario for the exploration of domain adaptation techniques.
Learned Pre-Processing for Automatic Diabetic Retinopathy Detection on Eye Fundus Images
Diabetic Retinopathy is the leading cause of blindness in the working-age population of the world.
Self-supervised blur detection from synthetically blurred scenes
Blur detection aims at segmenting the blurred areas of a given image.
O-MedAL: Online Active Deep Learning for Medical Image Analysis
Our online method enhances performance of its underlying baseline deep network.
UOLO - automatic object detection and segmentation in biomedical images
We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images.
On the Duality Between Retinex and Image Dehazing
Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog.
Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis
Specifically, we apply the \emph{shades of gray} color constancy technique to color-normalize the entire training set of images, while retaining the estimated illuminants.
Towards Adversarial Retinal Image Synthesis
These pairs are then used to learn a mapping from a binary vessel tree to a new retinal image.
