Spatial-Aware Transformer-GRU Framework for Enhanced Glaucoma Diagnosis from 3D OCT Imaging

Information Science, Computer Science, Artificial Intelligence, Medicine, Ophthalmology, Medical Imaging,
Glaucoma detection   3D optical coherence tomography   Transformer   gated recurrent units   spatial coherence   artificial intelligence   clinical decision support  

1. Academic Background—Innovative Diagnostic Tools Urgently Needed for Early Glaucoma Screening Glaucoma is one of the major diseases leading to irreversible blindness worldwide. According to studies such as [31], glaucoma is characterized by hidden early symptoms and irreversible visual impairment, making early detection and intervention crucial. ...

CryoTEN: Efficiently Enhancing Cryo-EM Density Maps Using Transformers

Information Science, Computer Science, Artificial Intelligence, Life Sciences, Biochemistry, Bioengineering, Biophysics,
Cryo-EM   density map enhancement   Transformer   deep learning   protein structure   image processing   bioinformatics  

Academic Background Cryogenic Electron Microscopy (Cryo-EM) is a crucial experimental technique for determining the structures of macromolecules such as proteins. However, the effectiveness of Cryo-EM is often hindered by noise and missing density values caused by experimental conditions such as low contrast and conformational heterogeneity. Althou...

DRTN: Dual Relation Transformer Network with Feature Erasure and Contrastive Learning for Multi-Label Image Classification

Information Science, Computer Science, Artificial Intelligence,
Multi-label image classification   Transformer   Pseudo-region   Feature erasure   Contrastive learning  

New Breakthrough in Multi-Label Image Classification: Dual Relation Transformer Network Academic Background Multi-Label Image Classification (MLIC) is a fundamental yet highly challenging problem in the field of computer vision. Unlike single-label image classification, MLIC aims to assign multiple labels to objects within a single image. Due to th...

Learning with Enriched Inductive Biases for Vision-Language Models

Information Science, Computer Science, Artificial Intelligence,
Vision Language Models   Inductive Biases   Few-shot Adaptation   Transformer  

Learning with Enriched Inductive Biases for Vision-Language Models Research Background and Problem Statement In recent years, Vision-Language Models (VLMs) have made significant progress in the fields of computer vision and natural language processing. These models are pre-trained on large-scale image-text pairs to construct a unified multimodal re...

A Mutual Supervision Framework for Referring Expression Segmentation and Generation

A Mutual Supervision Framework for Referring Expression Segmentation and Generation

Information Science, Computer Science, Artificial Intelligence,
Referring Expression Segmentation   Referring Expression Generation   Mutual Supervision   Transformer   Computer Vision   Natural Language Processing   Image Segmentation  

A Mutual Supervision Framework for Referring Expression Segmentation and Generation Research Background and Problem Statement In recent years, vision-language interaction technology has made remarkable progress in the field of artificial intelligence. Among these advancements, referring expression segmentation (RES) and referring expression generat...