Cancer Detection

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13 papers in the last four weeks, against 1 the four weeks before. 0.2% of all new papers.

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NAMD: Virtual Follow-up Computed Tomography Synthesis via Nodule-Aligned Multimodal Diffusion Models for Early Lung Cancer Diagnosis
Mar 16, 2026James Song, Yifan Wang, Chuan Zhou +1Lung-Nodule Artificial Intelligence Cancer Detection
University of Michigan · University of Michigan Medical School
Segmentation and Classification of Pap Smear Images for Cervical Cancer Detection Using Deep Learning
Aug 25, 2025Nisreen Albzour, Sarah S. LamCancer Detection Deep Learning
Binghamton University, Binghamton, NY, USA
Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification
Jun 23, 2025Ciro Listone, Aniello MuranoSkin Lesion Classification Melanoma
University of Naples Federico II, Naples, Italy.
Cohort-attention Evaluation Metrics for Tied Data
Mar 17, 2025Dongjing Jiang, Qingchong JiaoEvaluation Metrics Screening
DeepCox, Canada
Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers
Dec 4, 2021Renzhen Wang, De cai, Kaiwen Xiao +3Skin Lesion Classification Medical Image Classification
School of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, China · Tencent AI Lab, Shenzhen, China · ByteDance, Shenzhen, China +4

Mar 16, 2026, cs.CVJ/K move · Enter open · S save

NAMD: Virtual Follow-up Computed Tomography Synthesis via Nodule-Aligned Multimodal Diffusion Models for Early Lung Cancer Diagnosis

James Song, Yifan Wang, Chuan Zhou, Liyue Shen

University of Michigan · University of Michigan Medical School

Lung-Nodule Artificial Intelligence Cancer Detection Cone-Beam Computed Tomography Multimodal Diffusion Models

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Lung cancer remains the leading cause of cancer-related mortality worldwide, with survival outcomes critically dependent on early and accurate detection. When low-dose computed tomography (LDCT) findings are indeterminate, clinicians typically defer diagnosis pending follow-up CT imaging obtained up to 12 months later, inevitably delaying treatment for patients with malignant nodules. To address this clinical gap, we propose Nodule-Aligned Multimodal (Latent) Diffusion (NAMD), a novel generative framework that synthesizes one-year follow-up nodule CT images conditioned on the baseline CT scan, quantitative nodule biomarkers, and patient-level Electronic Health Records (EHR), enabling timely prediction of nodule malignant progression without requiring actual follow-up scans. NAMD introduces two key contributions: (i) a nodule-aligned latent space regularized so that embedding distances reflect clinically meaningful biomarker changes, and (ii) an LLM-driven multimodal conditioning mechanism encoding heterogeneous EHR data into the diffusion backbone. Evaluated on the National Lung Screening Trial (NLST), our method's synthetic follow-up images achieve an AUROC of 0.805 and an AUPRC of 0.346 for lung nodule malignancy prediction, outperforming both the baseline LDCT performance without virtual follow-up generation, and existing state-of-the-art conditional generation methods, while maintaining competitive image quality. These findings suggest that NAMD enables earlier and more accurate lung cancer diagnosis by capturing clinically meaningful features of nodule progression.

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Latest papers 65

NAMD: Virtual Follow-up Computed Tomography Synthesis via Nodule-Aligned Multimodal Diffusion Models for Early Lung Cancer Diagnosis

Segmentation and Classification of Pap Smear Images for Cervical Cancer Detection Using Deep Learning

Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification

Cohort-attention Evaluation Metrics for Tied Data

Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers