arXiv AI
Explainable Multimodal AI Approach to Reduce Unnecessary Breast Biopsies
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Hugging Face Trending Papers
Oct 26
Explainable Multimodal AI Approach to Reduce Unnecessary Breast Biopsies
Recently, there has been rise in multimodal vision-language models for computer-aided diagnosis (CAD), yet their application in breast diagnosis, particularly for predicting biopsy outcomes remains li...
arXiv Computer Vision
Oct 12
Multimodal Fusion for Fine-Grained Classification of Breast Fibroadenoma and Phyllodes Tumors
The paper introduces the FAPT-M Dataset, a pathology‑confirmed multimodal collection of 910 breast patients that includes ultrasound images, structured clinical attributes, and diagnostic descriptions. It proposes a clinically guided multimodal framework that fuses DenseNet visual encoding, CLIP‑inspired text encoding, and lightweight clinical encoding, enhanced by clinical‑conditioned adaptive modulation, cross‑modal Transformer fusion, and dual‑path representation learning. The method achieves 77.64% accuracy, 73.38% F1‑score, and 89.74% AUC, outperforming CNN, Transformer, and vision‑language baselines, and demonstrates the value of three‑modality fusion through ablation and class‑balanced studies.
arXiv Computer Vision
Sep 25
OncoVision: Integrating Mammography and Clinical Data through Attention-Driven Multimodal AI for Enhanced Breast Cancer Diagnosis
OncoVision is a privileged‑information training framework that learns from mammography images and clinical data during training but performs inference using only mammographic images. It employs an attention‑based encoder‑decoder to jointly segment masses, calcifications, axillary findings, and breast tissue, and predicts ten structured clinical features such as BI‑RADS. Two late‑fusion strategies (Independent and Dependent) integrate imaging, radiomic, and clinical information to improve diagnostic precision, and a retrospective multi‑reader study showed higher diagnostic confidence, reduced reading time, and segmentation accuracy comparable to or better than radiologists.
arXiv AI
Jun 30
IMCBench: A benchmark for multimodal LLMs in Image-grounded Medical Conversations
arXiv:2606. 28556v1 Announce Type: new Abstract: Recent advances in large language models and vision-language models have enabled reasoning over multimodal data, offering opportunities for clinical applications such as decision support and triaging.
arXiv Computer Vision
Sep 23
Mammo-LIFE: Longitudinal Mammographic Imaging and Clinical Feature Enrichment for Post-Radiotherapy Outcome Prediction
arXiv:2609.26443v1 Announce Type: new Abstract: Recent advances in Artificial Intelligence (AI)-powered Computer-Aided Diagnosis (CAD) systems have substantially improved breast cancer screening, dia...
Hugging Face Trending Papers
Jun 3
BreastGPT: A Multimodal Large Language Model for the Full Spectrum of Breast Cancer Clinical Routine
Breast cancer remains a leading cause of cancer-related mortality among women. Its clinical management requires multimodal reasoning across a clinical workflow that spans \textit{screening}, \textit{diagnosis} and \textit{treatment planning}, where each stage involves distinct imaging modalities, task objectives, and reasoning patterns.