Search Results for author:
Found 8 papers, 2 papers with code
Benchmarking Image Transformers for Prostate Cancer Detection from Ultrasound Data
In this work, we present a detailed study of several image transformer architectures for both ROI-scale and multi-scale classification, and a comparison of the performance of CNNs and transformers for ultrasound-based prostate cancer classification.
Manifold DivideMix: A Semi-Supervised Contrastive Learning Framework for Severe Label Noise
However, their performance degrades when training data contains noisy labels, leading to poor generalization on the test set.
Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification
In the past few years, deep learning-based models have been proven to be efficient on the PCa classification task and can be successfully used to support radiologists during the diagnostic process.
TRUSformer: Improving Prostate Cancer Detection from Micro-Ultrasound Using Attention and Self-Supervision
We analyze this method using a dataset of micro-ultrasound acquired from 578 patients who underwent prostate biopsy, and compare our model to baseline models and other large-scale studies in the literature.
Self-Supervised Learning with Limited Labeled Data for Prostate Cancer Detection in High Frequency Ultrasound
Our method outperforms baseline supervised learning approaches, generalizes well between different data centers, and scale well in performance as more unlabeled data are added, making it a promising approach for future research using large volumes of unlabeled data.
Towards Confident Detection of Prostate Cancer using High Resolution Micro-ultrasound
We train a deep model using a co-teaching paradigm to handle noise in labels, together with an evidential deep learning method for uncertainty estimation.
Deep Information Theoretic Registration
This paper establishes an information theoretic framework for deep metric based image registration techniques.
Semi-Supervised Deep Metrics for Image Registration
In this paper, we propose a strategy for learning such metrics from roughly aligned training data.
