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Smoking & Tobacco

Latest AI and machine learning research in smoking & tobacco for healthcare professionals.

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Dual-level Fuzzy Learning with Patch Guidance for Image Ordinal Regression

Ordinal regression bridges regression and classification by assigning objects to ordered classes. ...

RobSurv: Vector Quantization-Based Multi-Modal Learning for Robust Cancer Survival Prediction

Cancer survival prediction using multi-modal medical imaging presents a critical challenge in onco...

Backdoor Attacks Against Patch-based Mixture of Experts

As Deep Neural Networks (DNNs) continue to require larger amounts of data and computational power,...

Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Study

Multiple Instance Learning (MIL) has emerged as the best solution for Whole Slide Image (WSI) clas...

T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT

Recent advancements in large language models have demonstrated how chain-of-thought (CoT) and rein...

Neuroevolution of Self-Attention Over Proto-Objects

Proto-objects - image regions that share common visual properties - offer a promising alternative ...

Attention-enabled Explainable AI for Bladder Cancer Recurrence Prediction

Non-muscle-invasive bladder cancer (NMIBC) is a relentless challenge in oncology, with recurrence ...

Explaining Vision GNNs: A Semantic and Visual Analysis of Graph-based Image Classification

Graph Neural Networks (GNNs) have emerged as an efficient alternative to convolutional approaches ...

Masked strategies for images with small objects

The hematology analytics used for detection and classification of small blood components is a sign...

Distilling semantically aware orders for autoregressive image generation

Autoregressive patch-based image generation has recently shown competitive results in terms of ima...

4D Multimodal Co-attention Fusion Network with Latent Contrastive Alignment for Alzheimer's Diagnosis

Multimodal neuroimaging provides complementary structural and functional insights into both human ...

Exploring Cognitive and Aesthetic Causality for Multimodal Aspect-Based Sentiment Analysis

Multimodal aspect-based sentiment classification (MASC) is an emerging task due to an increase in ...

Human-Imperceptible Physical Adversarial Attack for NIR Face Recognition Models

Near-infrared (NIR) face recognition systems, which can operate effectively in low-light condition...

M-TabNet: A Multi-Encoder Transformer Model for Predicting Neonatal Birth Weight from Multimodal Data

Birth weight (BW) is a key indicator of neonatal health, with low birth weight (LBW) linked to inc...

ChatEXAONEPath: An Expert-level Multimodal Large Language Model for Histopathology Using Whole Slide Images

Recent studies have made significant progress in developing large language models (LLMs) in the me...

Embedding Radiomics into Vision Transformers for Multimodal Medical Image Classification

Background: Deep learning has significantly advanced medical image analysis, with Vision Transform...

CDUPatch: Color-Driven Universal Adversarial Patch Attack for Dual-Modal Visible-Infrared Detectors

Adversarial patches are widely used to evaluate the robustness of object detection systems in real...

GPS: Distilling Compact Memories via Grid-based Patch Sampling for Efficient Online Class-Incremental Learning

Online class-incremental learning aims to enable models to continuously adapt to new classes with ...

Patch and Shuffle: A Preprocessing Technique for Texture Classification in Autonomous Cementitious Fabrication

Autonomous fabrication systems are transforming construction and manufacturing, yet they remain vu...

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