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Found 10 papers, 4 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.
CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification
On the other hand, acquiring extensive datasets with localized labels for training is not feasible.
Contrastive Learning
Histopathological Image Classification
+2
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.
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.
Be Your Own Best Competitor! Multi-Branched Adversarial Knowledge Transfer
The proposed method has been devoted to both lightweight image classification and encoder-decoder architectures to boost the performance of small and compact models without incurring extra computational overhead at the inference process.
Lightweight Residual Densely Connected Convolutional Neural Network
Extremely efficient convolutional neural network architectures are one of the most important requirements for limited-resource devices (such as embedded and mobile devices).
Pointwise Attention-Based Atrous Convolutional Neural Networks
With the rapid progress of deep convolutional neural networks, in almost all robotic applications, the availability of 3D point clouds improves the accuracy of 3D semantic segmentation methods.
Multi-Modal Attention-based Fusion Model for Semantic Segmentation of RGB-Depth Images
One of the high-level tasks in 3D scene understanding is semantic segmentation of RGB-Depth images.
Ranked #5 on
Semantic Segmentation
on Stanford2D3D - RGBD
