Search Results for author:
Found 10 papers, 2 papers with code
Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection
In this study, we introduce a novel component segmentation model for LA detection that leverages a few labeled samples and unlabeled images sharing logical constraints.
Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification
Whole slide image (WSI) classification is a fundamental task for the diagnosis and treatment of diseases; but, curation of accurate labels is time-consuming and limits the application of fully-supervised methods.
CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification
Few-shot classification is a challenging problem that aims to learn a model that can adapt to unseen classes given a few labeled samples.
Uncertainty-Aware Semi-Supervised Few Shot Segmentation
During inference, query segmentation is predicted using prototypes from both support and unlabeled images including low-level features of the query images.
Content Preserving Image Translation with Texture Co-occurrence and Spatial Self-Similarity for Texture Debiasing and Domain Adaptation
Models trained on datasets with texture bias usually perform poorly on out-of-distribution samples since biased representations are embedded into the model.
Self-Supervised Learning based CT Denoising using Pseudo-CT Image Pairs
The proposed ensemble noise model can generate realistic CT noise, and thus our method significantly improves the denoising performance existing denoising models trained by supervised- and self-supervised learning.
Mixing-AdaSIN: Constructing a De-biased Dataset using Adaptive Structural Instance Normalization and Texture Mixing
Our experiments show that classifiers trained with de-biased generated images report improved in-distribution performance and generalization on an external COVID-19 dataset.
Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation
In this paper, we propose a 3D few shot segmentation framework for accurate organ segmentation using limited training samples of the target organ annotation.
Multiple Instance Learning with Center Embeddings for Histopathology Classification
To address this, recent methods have considered WSI classification as a Multiple Instance Learning (MIL) problem often with a multi-stage process for learning instance and slide level features.
Few-Shot Relation Learning with Attention for EEG-based Motor Imagery Classification
In addition to the unified learning of feature similarity and a few shot classifier, our method leads to emphasize informative features in support data relevant to the query data, which generalizes better on unseen subjects.
