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Found 8 papers, 5 papers with code
Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptive Object Detection
Domain adaptation methods for object detection (OD) strive to mitigate the impact of distribution shifts by promoting feature alignment across source and target domains.
Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction
We use an attention-based approach that weighs the importance of different slide regions in producing the final classification results.
Multi-Source Domain Adaptation for Object Detection with Prototype-based Mean-teacher
Given the use of prototypes, the number of parameters required for our PMT method does not increase significantly with the number of source domains, thus reducing memory issues and possible overfitting.
Multi-Source Unsupervised Domain Adaptation
object-detection
+2
Semi-Weakly Supervised Object Detection by Sampling Pseudo Ground-Truth Boxes
Semi- and weakly-supervised learning have recently attracted considerable attention in the object detection literature since they can alleviate the cost of annotation needed to successfully train deep learning models.
Spine intervertebral disc labeling using a fully convolutional redundant counting model
Labeling intervertebral discs is relevant as it notably enables clinicians to understand the relationship between a patient's symptoms (pain, paralysis) and the exact level of spinal cord injury.
End-to-End Discriminative Deep Network for Liver Lesion Classification
In the present work we introduce an end-to-end deep learning approach to assist in the discrimination between liver metastases from colorectal cancer and benign cysts in abdominal CT images of the liver.
Multi-Level Batch Normalization In Deep Networks For Invasive Ductal Carcinoma Cell Discrimination In Histopathology Images
Breast cancer is the most diagnosed cancer and the most predominant cause of death in women worldwide.
Baseline wander removal methods for ECG signals: A comparative study
Cardiovascular diseases are the leading cause of death worldwide, accounting for 17. 3 million deaths per year.
