Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer Using Generative Artificial Intelligence

• URL: http://arxiv.org/abs/2501.19176v1
• Date: Fri, 31 Jan 2025 14:41:17 GMT
• Title: Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer Using Generative Artificial Intelligence
• Authors: Aurora Rofena, Claudia Lucia Piccolo, Bruno Beomonte Zobel, Paolo Soda, Valerio Guarrasi,
• Abstract summary: Full-Field Digital Mammography (FFDM) is the primary imaging modality for routine breast cancer screening.<n>CESM, a second-level imaging technique, offers enhanced accuracy in tumor detection.<n>CESM is typically reserved for select cases, leaving many patients to rely solely on FFDM.<n>We introduce a multimodal, multi-view deep learning approach for virtual biopsy.
• Score: 0.8714814768600079
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Full-Field Digital Mammography (FFDM) is the primary imaging modality for routine breast cancer screening; however, its effectiveness is limited in patients with dense breast tissue or fibrocystic conditions. Contrast-Enhanced Spectral Mammography (CESM), a second-level imaging technique, offers enhanced accuracy in tumor detection. Nonetheless, its application is restricted due to higher radiation exposure, the use of contrast agents, and limited accessibility. As a result, CESM is typically reserved for select cases, leaving many patients to rely solely on FFDM despite the superior diagnostic performance of CESM. While biopsy remains the gold standard for definitive diagnosis, it is an invasive procedure that can cause discomfort for patients. We introduce a multimodal, multi-view deep learning approach for virtual biopsy, integrating FFDM and CESM modalities in craniocaudal and mediolateral oblique views to classify lesions as malignant or benign. To address the challenge of missing CESM data, we leverage generative artificial intelligence to impute CESM images from FFDM scans. Experimental results demonstrate that incorporating the CESM modality is crucial to enhance the performance of virtual biopsy. When real CESM data is missing, synthetic CESM images proved effective, outperforming the use of FFDM alone, particularly in multimodal configurations that combine FFDM and CESM modalities. The proposed approach has the potential to improve diagnostic workflows, providing clinicians with augmented intelligence tools to improve diagnostic accuracy and patient care. Additionally, as a contribution to the research community, we publicly release the dataset used in our experiments, facilitating further advancements in this field.

Related papers

• Integrating AI for Human-Centric Breast Cancer Diagnostics: A Multi-Scale and Multi-View Swin Transformer Framework [5.211860566766601]
  The paper focuses on the integration of AI within a Human-Centric workflow to enhance breast cancer diagnostics. We propose a hybrid, multi-scale and multi-view Swin Transformer-based framework (MSMV-Swin) that enhances diagnostic robustness and accuracy.
  arXiv  Detail & Related papers  (2025-03-17T15:48:56Z)
• Multimodal AI-driven Biomarker for Early Detection of Cancer Cachexia [14.27396467108753]
  Cancer cachexia is a multifactorial syndrome characterized by progressive muscle wasting, metabolic dysfunction, and systemic inflammation. There is no single definitive biomarker for cachexia. This study proposes a multimodal AI-based biomarker for early cancer cachexia detection.
  arXiv  Detail & Related papers  (2025-03-09T22:32:37Z)
• Cross-modality image synthesis from TOF-MRA to CTA using diffusion-based models [36.136619420474766]
  Computed Tomography Angiography (CTA) and Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) are two common non-invasive angiography techniques. This study explores diffusion-based image-to-image translation models to generate synthetic CTA images from TOF-MRA input.
  arXiv  Detail & Related papers  (2024-09-16T08:43:37Z)
• MMFusion: Multi-modality Diffusion Model for Lymph Node Metastasis Diagnosis in Esophageal Cancer [13.74067035373274]
  We introduce a multi-modal heterogeneous graph-based conditional feature-guided diffusion model for lymph node metastasis diagnosis based on CT images. We propose a masked relational representation learning strategy, aiming to uncover the latent prognostic correlations and priorities of primary tumor and lymph node image representations.
  arXiv  Detail & Related papers  (2024-05-15T17:52:00Z)
• Optimizing Synthetic Correlated Diffusion Imaging for Breast Cancer Tumour Delineation [71.91773485443125]
  We show that the best AUC is achieved by the CDI$s$ - optimized modality, outperforming the best gold-standard modality by 0.0044. Notably, the optimized CDI$s$ modality also achieves AUC values over 0.02 higher than the Unoptimized CDI$s$ value.
  arXiv  Detail & Related papers  (2024-05-13T16:07:58Z)
• Super-resolution of biomedical volumes with 2D supervision [84.5255884646906]
  Masked slice diffusion for super-resolution exploits the inherent equivalence in the data-generating distribution across all spatial dimensions of biological specimens. We focus on the application of SliceR to stimulated histology (SRH), characterized by its rapid acquisition of high-resolution 2D images but slow and costly optical z-sectioning.
  arXiv  Detail & Related papers  (2024-04-15T02:41:55Z)
• Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic Segmentation [48.107348956719775]
  We introduce Mask-Enhanced SAM (M-SAM), an innovative architecture tailored for 3D tumor lesion segmentation. We propose a novel Mask-Enhanced Adapter (MEA) within M-SAM that enriches the semantic information of medical images with positional data from coarse segmentation masks. Our M-SAM achieves high segmentation accuracy and also exhibits robust generalization.
  arXiv  Detail & Related papers  (2024-03-09T13:37:02Z)
• End-to-End Breast Cancer Radiotherapy Planning via LMMs with Consistency Embedding [47.360760580820966]
  We present RO-LMM, a comprehensive large multimodal model (LMM) tailored for the field of radiation oncology. This model effectively manages a series of tasks within the clinical workflow, including clinical context summarization, radiation treatment plan suggestion, and plan-guided target volume segmentation. We present a novel Consistency Embedding Fine-Tuning (CEFTune) technique, which boosts LMM's robustness to noisy inputs while preserving the consistency of handling clean inputs.
  arXiv  Detail & Related papers  (2023-11-27T14:49:06Z)
• Intelligent Breast Cancer Diagnosis with Heuristic-assisted Trans-Res-U-Net and Multiscale DenseNet using Mammogram Images [0.0]
  Breast cancer (BC) significantly contributes to cancer-related mortality in women. accurately distinguishing malignant mass lesions remains challenging. We propose a novel deep learning approach for BC screening utilizing mammography images.
  arXiv  Detail & Related papers  (2023-10-30T10:22:14Z)
• Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanism [15.453470023481932]
  Contrast-enhanced MRI (CE-MRI) provides superior differentiation between tumors and invaded healthy tissue. The use of gadolinium-based contrast agents (GBCA) to obtain CE-MRI may be associated with nephrogenic systemic fibrosis and may lead to bioaccumulation in the brain. To reduce the use of contrast agents, diffusion-weighted imaging (DWI) is emerging as a key imaging technique.
  arXiv  Detail & Related papers  (2023-07-03T09:46:12Z)
• Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging [59.79875531898648]
  DeepGlioma is an artificial-intelligence-based diagnostic screening system. DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy.
  arXiv  Detail & Related papers  (2023-03-23T18:50:18Z)
• Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment [50.89811515036067]
  Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate. We propose a practical AI system that enables five subclassifications of GC pathology, which can be directly matched to general GC treatment guidance.
  arXiv  Detail & Related papers  (2022-02-17T08:33:52Z)
• RCA-IUnet: A residual cross-spatial attention guided inception U-Net model for tumor segmentation in breast ultrasound imaging [0.6091702876917281]
  The article introduces an efficient residual cross-spatial attention guided inception U-Net (RCA-IUnet) model with minimal training parameters for tumor segmentation. The RCA-IUnet model follows U-Net topology with residual inception depth-wise separable convolution and hybrid pooling layers. Cross-spatial attention filters are added to suppress the irrelevant features and focus on the target structure.
  arXiv  Detail & Related papers  (2021-08-05T10:35:06Z)
• Learned super resolution ultrasound for improved breast lesion characterization [52.77024349608834]
  Super resolution ultrasound localization microscopy enables imaging of the microvasculature at the capillary level. In this work we use a deep neural network architecture that makes effective use of signal structure to address these challenges. By leveraging our trained network, the microvasculature structure is recovered in a short time, without prior PSF knowledge, and without requiring separability of the UCAs.
  arXiv  Detail & Related papers  (2021-07-12T09:04:20Z)
• Spectral-Spatial Recurrent-Convolutional Networks for In-Vivo Hyperspectral Tumor Type Classification [49.32653090178743]
  We demonstrate the feasibility of in-vivo tumor type classification using hyperspectral imaging and deep learning. Our best model achieves an AUC of 76.3%, significantly outperforming previous conventional and deep learning methods.
  arXiv  Detail & Related papers  (2020-07-02T12:00:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.