Search: lung - ai.jp.net

product #video 📝 BlogAnalyzed: Jan 16, 2026 01:21

AI-Generated Victorian London Comes to Life in Thrilling Video

Published:Jan 15, 2026 19:50

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1 min read

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r/midjourney

Analysis

Get ready to be transported! This incredible video, crafted with Midjourney and Veo 3.1, plunges viewers into a richly detailed Victorian London populated by fantastical creatures. The ability to make trolls 'talk' convincingly is a truly exciting leap forward for AI-generated storytelling!

Key Takeaways

•The video is part of 'The Drakewood Monster Shelter' series, set in Victorian London.
•The project utilizes Midjourney for character and environment creation.
•Veo 3.1 is the primary tool for generating the video, especially for lifelike dialogue.

Reference

“Video almost 100% Veo 3.1 (only gen that can make Trolls talk and make it look normal).”

Permalink r/midjourney

Research Paper #Medical Imaging, AI in Healthcare, Lung Cancer 🔬 ResearchAnalyzed: Jan 3, 2026 16:41

Interpretable AI for Lung Cancer Screening

Published:Dec 31, 2025 00:23

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1 min read

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ArXiv

Analysis

This paper addresses the limitations of current lung cancer screening methods by proposing a novel approach to connect radiomic features with Lung-RADS semantics. The development of a radiological-biological dictionary is a significant step towards improving the interpretability of AI models in personalized medicine. The use of a semi-supervised learning framework and SHAP analysis further enhances the robustness and explainability of the proposed method. The high validation accuracy (0.79) suggests the potential of this approach to improve lung cancer detection and diagnosis.

Key Takeaways

•Proposes a radiological-biological dictionary to link radiomic features with Lung-RADS semantics.
•Employs a semi-supervised learning framework to improve generalizability.
•Utilizes SHAP analysis to identify key radiomic features corresponding to Lung-RADS semantics.
•Achieves a validation accuracy of 0.79, demonstrating the potential for improved lung cancer screening.

Reference

“The optimal pipeline (ANOVA feature selection with a support vector machine) achieved a mean validation accuracy of 0.79.”

Permalink ArXiv

Paper #Medical Imaging, Deep Learning, Lung Cancer 🔬 ResearchAnalyzed: Jan 3, 2026 15:40

Virtual-Eyes Improves Foundation Model Performance for Lung Cancer Risk Prediction

Published:Dec 30, 2025 15:34

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1 min read

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ArXiv

Analysis

This paper investigates the impact of a quality control pipeline, Virtual-Eyes, on deep learning models for lung cancer risk prediction using low-dose CT scans. The study is significant because it quantifies the effect of preprocessing on different types of models, including generalist foundation models and specialist models. The findings highlight that anatomically targeted quality control can improve the performance of generalist models while potentially disrupting specialist models. This has implications for the design and deployment of AI-powered diagnostic tools in clinical settings.

Key Takeaways

•Virtual-Eyes, a CT quality-control pipeline, improves the performance of generalist foundation models (e.g., RAD-DINO) for lung cancer risk prediction.
•Specialist models (e.g., Sybil, ResNet-18) may be negatively impacted by Virtual-Eyes, suggesting context dependence and shortcut learning.
•The study highlights the importance of preprocessing and its differential impact on various model types in medical imaging AI.

Reference

“Virtual-Eyes improves RAD-DINO slice-level AUC from 0.576 to 0.610 and patient-level AUC from 0.646 to 0.683 (mean pooling) and from 0.619 to 0.735 (max pooling), with improved calibration (Brier score 0.188 to 0.112).”

Permalink ArXiv

Medical Imaging #Chest X-ray Analysis, Medical Image Segmentation, Deep Learning 🔬 ResearchAnalyzed: Jan 3, 2026 16:15

MedSAM-based Lung Masking for Chest X-ray Classification

Published:Dec 28, 2025 21:56

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1 min read

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ArXiv

Analysis

This paper addresses the challenge of automated chest X-ray interpretation by leveraging MedSAM for lung region extraction. It explores the impact of lung masking on multi-label abnormality classification, demonstrating that masking strategies should be tailored to the specific task and model architecture. The findings highlight a trade-off between abnormality-specific classification and normal case screening, offering valuable insights for improving the robustness and interpretability of CXR analysis.

Key Takeaways

•MedSAM is used for lung region extraction in chest X-ray analysis.
•Lung masking strategies impact classification performance, with trade-offs between abnormality detection and normal case screening.
•Masking should be tailored to the model architecture and clinical objective.

Reference

“Lung masking should be treated as a controllable spatial prior selected to match the backbone and clinical objective, rather than applied uniformly.”

Permalink ArXiv

Paper #Medical AI 🔬 ResearchAnalyzed: Jan 3, 2026 19:47

AI for Early Lung Disease Detection

Published:Dec 27, 2025 16:50

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1 min read

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ArXiv

Analysis

This paper is significant because it explores the application of deep learning, specifically CNNs and other architectures, to improve the early detection of lung diseases like COVID-19, lung cancer, and pneumonia using chest X-rays. This is particularly impactful in resource-constrained settings where access to radiologists is limited. The study's focus on accuracy, precision, recall, and F1 scores demonstrates a commitment to rigorous evaluation of the models' performance, suggesting potential for real-world diagnostic applications.

Key Takeaways

•Applies deep learning (CNNs, VGG16, InceptionV3, EfficientNetB0) to chest X-ray analysis for lung disease detection.
•Focuses on early detection of COVID-19, lung cancer, and pneumonia.
•Aims to provide rapid, accurate, and non-invasive diagnostic solutions.
•Emphasizes high accuracy, precision, recall, and F1 scores for model validation.
•Addresses the need for improved diagnostics in areas with limited healthcare resources.

Reference

“The study highlights the potential of deep learning methods in enhancing the diagnosis of respiratory diseases such as COVID-19, lung cancer, and pneumonia from chest x-rays.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Dec 25, 2025 09:19

Enhancing Lung Cancer Treatment Outcome Prediction through Semantic Feature Engineering Using Large Language Models

Published:Dec 25, 2025 05:00

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1 min read

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ArXiv ML

Analysis

This research paper presents a novel framework leveraging Large Language Models (LLMs) as Goal-oriented Knowledge Curators (GKC) to improve lung cancer treatment outcome prediction. The study addresses the challenges of sparse, heterogeneous, and contextually overloaded electronic health data. By converting laboratory, genomic, and medication data into task-aligned features, the GKC approach outperforms traditional methods and direct text embeddings. The results demonstrate the potential of LLMs in clinical settings, not as black-box predictors, but as knowledge curation engines. The framework's scalability, interpretability, and workflow compatibility make it a promising tool for AI-driven decision support in oncology, offering a significant advancement in personalized medicine and treatment planning. The use of ablation studies to confirm the value of multimodal data is also a strength.

Key Takeaways

•LLMs can be effectively used as Goal-oriented Knowledge Curators (GKC) for feature engineering.
•The GKC approach outperforms traditional methods in predicting lung cancer treatment outcomes.
•The framework offers a scalable and interpretable solution for AI-driven decision support in oncology.

Reference

“By reframing LLMs as knowledge curation engines rather than black-box predictors, this work demonstrates a scalable, interpretable, and workflow-compatible pathway for advancing AI-driven decision support in oncology.”

Permalink ArXiv ML

Research #Medical AI 🔬 ResearchAnalyzed: Jan 10, 2026 07:50

DGSAN: Enhancing Pulmonary Nodule Malignancy Prediction with AI

Published:Dec 24, 2025 02:47

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1 min read

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ArXiv

Analysis

This ArXiv paper introduces DGSAN, a novel AI model for predicting pulmonary nodule malignancy. The use of dual-graph spatiotemporal attention networks is a promising approach for improving diagnostic accuracy in this critical area.

Key Takeaways

•DGSAN is designed to predict the malignancy of pulmonary nodules.
•The model utilizes dual-graph spatiotemporal attention networks.
•The research aims to improve diagnostic accuracy in lung cancer detection.

Reference

“DGSAN leverages a dual-graph spatiotemporal attention network.”

Permalink ArXiv

Ethics #Healthcare AI 🔬 ResearchAnalyzed: Jan 10, 2026 07:55

Fairness in Lung Cancer Risk Models: A Critical Evaluation

Published:Dec 23, 2025 19:57

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1 min read

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ArXiv

Analysis

This ArXiv article likely investigates potential biases in AI models used for lung cancer screening. It's crucial to ensure these models provide equitable risk assessments across different demographic groups to prevent disparities in healthcare access.

Key Takeaways

•Focuses on fairness, a critical aspect of AI in healthcare.
•Examines risk estimation models for lung cancer screening.
•Highlights the importance of equitable access to screening based on model performance.

Reference

“The context mentions the article is sourced from ArXiv, indicating it is a pre-print research paper.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:08

Comparative Evaluation of Explainable Machine Learning Versus Linear Regression for Predicting County-Level Lung Cancer Mortality Rate in the United States

Published:Dec 10, 2025 23:33

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1 min read

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ArXiv

Analysis

This article focuses on a comparative analysis of explainable machine learning (ML) techniques against linear regression for predicting lung cancer mortality rates at the county level in the US. The study's significance lies in its potential to improve understanding of the factors contributing to lung cancer mortality and to inform public health interventions. The use of explainable ML is particularly noteworthy, as it aims to provide insights into the 'why' behind the predictions, which is crucial for practical application and trust-building. The source, ArXiv, indicates this is a pre-print or research paper, suggesting a rigorous methodology and data-driven approach.

Key Takeaways

•Focuses on comparing explainable ML with linear regression for predicting lung cancer mortality.
•Emphasizes the importance of explainability in ML for practical application and trust.
•Likely uses rigorous statistical methods and data-driven analysis.

Reference

“The study likely employs statistical methods to compare the performance of different models, potentially including metrics like accuracy, precision, recall, and F1-score. It would also likely delve into the interpretability of the ML models, assessing how well the models' decisions can be understood and explained.”

Permalink ArXiv

Research #AI, Cancer 🔬 ResearchAnalyzed: Jan 10, 2026 12:34

AI-Powered Analysis of Cell Interactions Predicts Lung Cancer Prognosis

Published:Dec 9, 2025 13:10

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1 min read

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ArXiv

Analysis

This research leverages AI to analyze complex biological data from multiplex microscopy, offering a potentially powerful tool for lung cancer diagnosis and prognosis. The study's focus on cell inter-relations highlights a shift towards understanding cancer at a systems level.

Key Takeaways

•AI is employed to analyze multiplex microscopy data.
•The focus is on understanding cell-cell interactions.
•The research aims to improve lung cancer prognosis.

Reference

“The article focuses on hierarchical analysis of cell inter-relations in multiplex microscopy for lung cancer prognosis.”

Permalink ArXiv

Research #Medical AI 🔬 ResearchAnalyzed: Jan 10, 2026 12:40

AI Detects Out-of-Distribution Data in Lung Cancer Segmentation

Published:Dec 9, 2025 03:49

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1 min read

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ArXiv

Analysis

This research explores a novel application of AI in medical imaging, specifically focusing on identifying data points that deviate from the expected distribution in lung cancer segmentation. The use of deep feature random forests for this task is a promising approach for improving the reliability of AI-driven diagnostic tools.

Key Takeaways

•The research focuses on the application of AI in medical imaging, specifically lung cancer segmentation.
•The core methodology involves using tumor-anchored deep feature random forests for out-of-distribution detection.
•The goal is to enhance the accuracy and reliability of AI-powered diagnostic tools.

Reference

“The article's source is ArXiv, indicating it is likely a pre-print of a scientific research paper.”

Permalink ArXiv

Research #Healthcare AI 🔬 ResearchAnalyzed: Jan 10, 2026 13:23

Explainable AI for Lung Cancer Classification: A Deep Learning Framework

Published:Dec 3, 2025 01:48

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1 min read

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ArXiv

Analysis

This research explores a hybrid approach combining DenseNet169 and SVM for lung cancer classification, a potentially valuable application of AI in healthcare. The explainable AI component enhances the trustworthiness and usability of the model by providing insights into its decision-making process.

Key Takeaways

•The paper focuses on a specific medical application of AI: lung cancer classification.
•The approach combines a convolutional neural network (DenseNet169) with a Support Vector Machine (SVM).
•The inclusion of explainable AI is a key feature, promoting transparency in the model's predictions.

Reference

“The study utilizes a hybrid deep learning framework.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:55

Enhancing Lung Cancer Treatment Outcome Prediction through Semantic Feature Engineering Using Large Language Models

Published:Dec 1, 2025 23:56

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1 min read

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ArXiv

Analysis

This article, sourced from ArXiv, focuses on using Large Language Models (LLMs) to improve the prediction of lung cancer treatment outcomes. The core idea revolves around semantic feature engineering, suggesting the application of LLMs to extract meaningful features from data to enhance predictive accuracy. The research likely explores how LLMs can understand and process complex medical information to provide better insights into treatment effectiveness.

Key Takeaways

•The research utilizes Large Language Models (LLMs) for lung cancer treatment outcome prediction.
•The approach involves semantic feature engineering to extract meaningful information.
•The goal is to improve the accuracy of predicting treatment effectiveness.

Reference

“The article's specific methodologies and findings are not available in this summary. Further investigation of the ArXiv paper is needed to understand the details of the semantic feature engineering process and the performance improvements achieved.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 08:45

Explainable Multi-Modal Deep Learning for Automatic Detection of Lung Diseases from Respiratory Audio Signals

Published:Nov 29, 2025 17:15

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1 min read

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ArXiv

Analysis

This article describes research on using explainable multi-modal deep learning to detect lung diseases from respiratory audio signals. The focus is on the explainability of the AI model, which is crucial for medical applications. The use of multi-modal data (likely combining audio with other data) suggests a potentially more robust and accurate diagnostic tool. The source, ArXiv, indicates this is a pre-print or research paper.

Key Takeaways

•Focus on explainable AI for medical applications.
•Utilizes multi-modal data for potentially improved accuracy.
•Research paper published on ArXiv.

Reference

“”

Permalink ArXiv

Research #Medical AI 🔬 ResearchAnalyzed: Jan 10, 2026 13:55

AI Revolutionizes Lung Cancer Screening: Outperforming Radiologists and Existing Models

Published:Nov 29, 2025 02:17

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1 min read

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ArXiv

Analysis

This article highlights a significant advance in medical AI, suggesting that AI-powered nodule detection surpasses human and algorithmic benchmarks. The study's findings have the potential to significantly improve early lung cancer detection and patient outcomes.

Key Takeaways

•AI demonstrates superior performance in lung nodule detection and diagnosis compared to radiologists.
•The AI model surpasses existing leading models and traditional screening methods based on size and growth.
•This research suggests the potential for more accurate and earlier detection of lung cancer through AI.

Reference

“AI Nodule Detection and Diagnosis Outperforms Radiologists, Leading Models, and Standards Beyond Size and Growth”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:50

LungNoduleAgent: A Collaborative Multi-Agent System for Precision Diagnosis of Lung Nodules

Published:Nov 26, 2025 04:19

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1 min read

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ArXiv

Analysis

This article introduces LungNoduleAgent, a multi-agent system designed for the precise diagnosis of lung nodules. The focus is on a collaborative approach, suggesting the use of multiple AI agents working together. The source being ArXiv indicates this is likely a research paper, detailing the system's architecture, methodology, and potentially, its performance. The topic is clearly within the realm of AI and medical imaging, specifically focusing on the application of AI for improved diagnostic accuracy in lung cancer detection.

Key Takeaways

Reference

“”

Permalink ArXiv

Healthcare #Artificial Intelligence, Medical Imaging, Osteoarthritis 📝 BlogAnalyzed: Jan 3, 2026 06:25

AI Predicts Future X-rays for Arthritis

Published:Oct 22, 2025 13:57

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1 min read

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ScienceDaily AI

Analysis

The article highlights a promising application of AI in healthcare, specifically for predicting the progression of osteoarthritis. The key strengths are the tool's ability to provide both visual forecasts and risk scores, offering a more comprehensive understanding of the disease. The mention of faster processing and potential expansion to other diseases suggests significant future impact. The article is concise and clearly explains the innovation and its potential benefits.

Key Takeaways

•AI developed at the University of Surrey predicts future knee X-rays.
•The tool provides visual forecasts and risk scores for osteoarthritis.
•It's faster and more interpretable than previous systems.
•Potential for expansion to other diseases like lung or heart disease.

Reference

“The article doesn't contain a direct quote, but the core idea is that the AI provides a 'visual forecast and a risk score, offering doctors and patients a clearer understanding of the disease.'”

Permalink ScienceDaily AI

Research #llm 🏛️ OfficialAnalyzed: Dec 24, 2025 11:43

Google AI Improves Lung Cancer Screening with Computer-Aided Diagnosis

Published:Mar 20, 2024 20:54

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1 min read

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Google Research

Analysis

This article from Google Research highlights the potential of AI in improving lung cancer screening. It emphasizes the importance of early detection through CT scans and the challenges associated with current screening methods, such as false positives and radiologist availability. The article mentions Google's previous work in developing ML models for lung cancer detection, suggesting a focus on automating and improving the accuracy of the screening process. The expansion of screening recommendations in the US further underscores the need for efficient and reliable diagnostic tools. The article sets the stage for further discussion on the specific advancements and performance of Google's AI-powered solution.

Key Takeaways

•Lung cancer is a leading cause of cancer deaths, and early detection is crucial.
•CT scans are effective for lung cancer screening but can lead to false positives.
•Google is developing AI models to improve the accuracy and efficiency of lung cancer detection.

Reference

“Lung cancer screening via computed tomography (CT), which provides a detailed 3D image of the lungs, has been shown to reduce mortality in high-risk populations by at least 20% by detecting potential signs of cancers earlier.”

Permalink Google Research

Research #Medical AI 👥 CommunityAnalyzed: Jan 10, 2026 16:51

AI-Powered Diagnostics Match Pathologist Accuracy in Lung Cancer Classification

Published:Apr 15, 2019 23:59

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1 min read

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Hacker News

Analysis

This news highlights a potentially significant advancement in medical diagnostics, showcasing the ability of AI to assist, or potentially match, the accuracy of human pathologists. The implications for faster, more accessible, and potentially more accurate diagnoses are considerable.

Key Takeaways

•AI demonstrates the ability to perform lung cancer classification at a pathologist's level of accuracy.
•This technology could lead to earlier and more accurate diagnoses.
•Potential for increased efficiency and reduced workload for pathologists.

Reference

“AI helps classify lung cancer at the pathologist level.”

Permalink Hacker News

AI-Generated Victorian London Comes to Life in Thrilling Video

Analysis

Key Takeaways

Interpretable AI for Lung Cancer Screening

Analysis

Key Takeaways

Virtual-Eyes Improves Foundation Model Performance for Lung Cancer Risk Prediction

Analysis

Key Takeaways

MedSAM-based Lung Masking for Chest X-ray Classification

Analysis

Key Takeaways

AI for Early Lung Disease Detection

Analysis

Key Takeaways

Enhancing Lung Cancer Treatment Outcome Prediction through Semantic Feature Engineering Using Large Language Models

Analysis

Key Takeaways

DGSAN: Enhancing Pulmonary Nodule Malignancy Prediction with AI

Analysis

Key Takeaways

Fairness in Lung Cancer Risk Models: A Critical Evaluation

Analysis

Key Takeaways

Comparative Evaluation of Explainable Machine Learning Versus Linear Regression for Predicting County-Level Lung Cancer Mortality Rate in the United States

Analysis

Key Takeaways

AI-Powered Analysis of Cell Interactions Predicts Lung Cancer Prognosis

Analysis

Key Takeaways

AI Detects Out-of-Distribution Data in Lung Cancer Segmentation

Analysis

Key Takeaways

Explainable AI for Lung Cancer Classification: A Deep Learning Framework

Analysis

Key Takeaways

Enhancing Lung Cancer Treatment Outcome Prediction through Semantic Feature Engineering Using Large Language Models

Analysis

Key Takeaways

Explainable Multi-Modal Deep Learning for Automatic Detection of Lung Diseases from Respiratory Audio Signals

Analysis

Key Takeaways

AI Revolutionizes Lung Cancer Screening: Outperforming Radiologists and Existing Models

Analysis

Key Takeaways

LungNoduleAgent: A Collaborative Multi-Agent System for Precision Diagnosis of Lung Nodules

Analysis

Key Takeaways

AI Predicts Future X-rays for Arthritis

Analysis

Key Takeaways

Google AI Improves Lung Cancer Screening with Computer-Aided Diagnosis

Analysis

Key Takeaways

AI-Powered Diagnostics Match Pathologist Accuracy in Lung Cancer Classification

Analysis

Key Takeaways

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