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Found 11 papers, 2 papers with code
Causal Scoring Medical Image Explanations: A Case Study On Ex-vivo Kidney Stone Images
To address this situation, we explore a technique for measuring the causal relationship between the features from the area of the object of interest in the images of a class and the output of a classifier.
FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation
This contribution presents a deep learning method for the segmentation of prostate zones in MRI images based on U-Net using additive and feature pyramid attention modules, which can improve the workflow of prostate cancer detection and diagnosis.
Assessing the performance of deep learning-based models for prostate cancer segmentation using uncertainty scores
This study focuses on comparing deep learning methods for the segmentation and quantification of uncertainty in prostate segmentation from MRI images.
A metric learning approach for endoscopic kidney stone identification
The proposed Guided Deep Metric Learning approach is based on a novel architecture which was designed to learn data representations in an improved way.
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification
Few-shot learning is a challenging area of research that aims to learn new concepts with only a few labeled samples of data.
Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and are Competitively Robust to Photometric Perturbations
Using PPs in the classification task enables case-based reasoning explanations for such output, thus making the model interpretable.
MACFE: A Meta-learning and Causality Based Feature Engineering Framework
Feature engineering has become one of the most important steps to improve model prediction performance, and to produce quality datasets.
Guided Deep Metric Learning
Deep Metric Learning (DML) methods have been proven relevant for visual similarity learning.
Interpretable Deep Learning Classifier by Detection of Prototypical Parts on Kidney Stones Images
Identifying the type of kidney stones can allow urologists to determine their formation cause, improving the early prescription of appropriate treatments to diminish future relapses.
Comparing feature fusion strategies for Deep Learning-based kidney stone identification
This contribution presents a deep-learning method for extracting and fusing image information acquired from different viewpoints with the aim to produce more discriminant object features.
On the in vivo recognition of kidney stones using machine learning
This pilot study compares the kidney stone recognition performances of six shallow machine learning methods and three deep-learning architectures which were tested with in-vivo images of the four most frequent urinary calculi types acquired with an endoscope during standard ureteroscopies.
