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Found 12 papers, 4 papers with code
RetMIL: Retentive Multiple Instance Learning for Histopathological Whole Slide Image Classification
At the local level, the WSI sequence is divided into multiple subsequences.
Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis
Histopathological whole slide images (WSIs) classification has become a foundation task in medical microscopic imaging processing.
Neural Implicit Surface Reconstruction for Freehand 3D Ultrasound Volumetric Point Clouds with Geometric Constraints
In this study, we introduce a self-supervised neural implicit surface reconstruction method to learn the signed distance functions (SDFs) from freehand 3D US volumetric point clouds.
The Whole Pathological Slide Classification via Weakly Supervised Learning
Due to its superior efficiency in utilizing annotations and addressing gigapixel-sized images, multiple instance learning (MIL) has shown great promise as a framework for whole slide image (WSI) classification in digital pathology diagnosis.
Automatic Diagnosis of Carotid Atherosclerosis Using a Portable Freehand 3D Ultrasound Imaging System
A total of 127 3D carotid artery scans were acquired using a portable 3D US system which consisted of a handheld US scanner and an electromagnetic tracking system.
HIRL: A General Framework for Hierarchical Image Representation Learning
This framework aims to learn multiple semantic representations for each image, and these representations are structured to encode image semantics from fine-grained to coarse-grained.
HCSC: Hierarchical Contrastive Selective Coding
In this framework, a set of hierarchical prototypes are constructed and also dynamically updated to represent the hierarchical semantic structures underlying the data in the latent space.
True or False: Does the Deep Learning Model Learn to Detect Rumors?
It is difficult for humans to distinguish the true and false of rumors, but current deep learning models can surpass humans and achieve excellent accuracy on many rumor datasets.
HAT4RD: Hierarchical Adversarial Training for Rumor Detection on Social Media
As such, the robustness and generalization of the current rumor detection model are put into question.
DropAttack: A Masked Weight Adversarial Training Method to Improve Generalization of Neural Networks
We compare the proposed method with other adversarial training methods and regularization methods, and our method achieves state-of-the-art on all datasets.
Exploiting Microblog Conversation Structures to Detect Rumors
Twitter users communicate by replying to other users{'} messages, forming a conversation structure.
Rumor Detection on Twitter Using Multiloss Hierarchical BiLSTM with an Attenuation Factor
Inspired by the hierarchical model and multitask learning, a multiloss hierarchical BiLSTM model with an attenuation factor is proposed in this paper.
