Digital Radiography and Breast Imaging

Últimos artículos

• M3D-Net: Hierarchical Coordination of Spatial Context, Feature Reuse, and Differential Attention for Mammography Classification

  Zheng Yu, Xinhang Li, Jiabao Gao, Boyang Wang, Xiang Li · 24 de septiembre de 2026

  Breast image classification requires local detail and global tissue context, yet these cues can weaken as representations deepen. We present M3D-Net, a mammography encoder that hierarchically coordinates multi-scale coordinate attention, bounded dynamic feature reuse, and differential attention thro…

• Foundation model embeddings capture pre-diagnostic changes on screening mammograms

  Kalina P. Slavkova, Eric Brattain, Aditya Gowd, Akash Pattnaik, Jean-Benoit Delbrouck, Matthew Morgan, Julie Bauml, Javid Abderezaei, Khan Siddiqui · 23 de septiembre de 2026

  Foundation model embeddings of screening mammograms may encode pre-diagnostic tissue change without task-specific adaptation. We tested whether embeddings move faster along a data-derived "cancer direction" in women later biopsied for cancer than in matched screen-negative controls, and whether this…

• Solving the Needle-in-a-Haystack Problem in Mammography Vision-Language Model with Differentiable Subset Sampling

  Young Seok Jeon, Beatrice Brown-Mulry, Rohan Satya Isaac, Anjana Dissanayaka, Theo Dapamede, Mohammadreza Chavoshi, Judy Gichoya, Hari Trivedi · 4 de septiembre de 2026

  There is growing interest in adopting CLIP-style vision--language model (VLM) pretraining for mammography. However, models that directly employ the standard CLIP architecture and training objective exhibit limited zero-shot performance in clinically important tasks such as cancer, finding-type, and …

• TRUST: Threshold-Recalibrated Uncertainty-Safe Training for Certified Dismissal in Breast Cancer Screening

  Parham Hajishafiezahramini, Matthew Hamilton, Edward Kendall, Gregory Doyle, Oscar Meruvia Pastor · 2 de septiembre de 2026

  Reducing the review of clearly cancer-negative screening mammograms could lower radiologist workload without compromising cancer detection. We propose a closed-loop threshold-aware training strategy in which the dismissal threshold is recalculated during training and used to penalize cancer-positive…

• MammoMix: Leveraging Mixture of Experts for Robust Mammogram Breast Detection

  Dinh Tan Nguyen, Hoang Quan Dang, Chen Zhang, Sai Ho Ling · 12 de agosto de 2026

  Breast lesion detection in mammography remains a challenging task due to variations in image quality, lesion appearance, and population demographics across datasets. While current object detectors such as YOLO and DETR achieve strong results on individual datasets, their performance often degrades w…

• CBCT-IQ: A Publicly Available Annotated Cone-Beam CT Dataset for Image Quality Assessment and Benchmarking

  Sepideh Hatamikia, Anna Breger, Clemens Karner, Birgit Pohn, Poorya MohammadiNasab, Martin Buschmann, Stephanie Nougaret, Laura Haddad, Ali Abbasian Ardakani, Afshin Mohammadi, Paul Apfaltrer, Wolfgang Birkfellner, Alfred Pohl, Ander Biguri, Gernot Kronreif, Carola-Bibiane Schönlieb, Tess Reynolds · 3 de agosto de 2026

  Medical image quality plays a critical role in diagnostic accuracy, especially in X-ray-based imaging modalities such as cone-beam computed tomography (CBCT), where image quality must be balanced against radiation dose. While expert visual evaluation remains the clinical standard for image quality e…

• A Diagnostic Gap Framework for Evaluating Reconstruction Fidelity in Weakly Supervised Mammography

  Vinceline Bertrand, Ionut Cardei · 24 de julio de 2026

  Weakly supervised pipelines for medical imaging have become increasingly popular over the years. These systems often include multiple stages and components, such as reconstruction, generation, and localization, yet standard evaluation metrics provide limited insight into whether clinically relevant …

• Exact and Calibrated Diffusion Reconstruction for Digital Breast Tomosynthesis

  Imade Bouftini · 15 de julio de 2026

  Limited-angle digital breast tomosynthesis (DBT) reconstructs a volume from a few low-dose projections over a narrow arc. At a representative nine-view, $25^{\circ}$ protocol more than 98% of image space is unmeasured, so a learned prior must supply structure in the missing wedge. Conditional diffus…

• Beyond Target Scores: Measuring Off-Target Drift in Diffusion-Based Medical Image Editing

  Todd Zhou · 14 de julio de 2026

  Diffusion models can now edit medical images in visually plausible ways, but the standard evaluation question is too narrow: did the target score increase? In clinical imaging, target findings are entangled with co-morbidities, acquisition effects, and selection bias, so a model can appear successfu…

• An Open-Source Monitoring Framework for Data Exploration and Progress Tracking in Multi-Center Radiology Studies

  Markus Bujotzek, Jonas Scherer, Stefan Denner, Peter Neher, Benjamin Hamm, Lorenz Feineis, Uenal Akuenal, Andreas Bucher, Tobias Penzkofer, Klaus Maier-Hein · 16 de junio de 2026

  Multi-center studies are crucial for advancing medical and radiological research. Data exploration, collaboration discovery, and study progress monitoring are essential for maximizing their potential. However, in practice these processes often rely on manual communication and shared tables, which qu…

• External Validation of Deep Learning Models for BI-RADS Breast Density Prediction from Ultrasound Images

  Yuxuan Chen, Arianna Bunnell, Yanqi Xu, Haoyan Yang, Thomas K. Wolfgruber, John A. Shepherd, Yiqiu Shen · 7 de mayo de 2026

  We externally validated three deep learning models (DenseNet121, ViT-B/32, and ResNet50) for predicting mammographic breast density from breast ultrasound exams on an independent cohort. The external validation set comprised 2,000 ultrasound exams, including 500 cancer cases defined by an initial ne…

• A Workflow to Efficiently Generate Dense Tissue Ground Truth Masks for Digital Breast Tomosynthesis

  Tamerlan Mustafaev, Oleg Kruglov, Margarita Zuley, Luana de Mero Omena, Guilherme Muniz de Oliveira, Vitor de Sousa Franca, Bruno Barufaldi, Robert Nishikawa, Juhun Lee · 15 de abril de 2026

  Digital breast tomosynthesis (DBT) is now the standard of care for breast cancer screening in the USA. Accurate segmentation of fibroglandular tissue in DBT images is essential for personalized risk estimation, but algorithm development is limited by scarce human-delineated training data. In this st…

• A Green Learning Approach to LDCT Image Restoration

  Wei Wang, Yixing Wu, C. -C. Jay Kuo · 24 de febrero de 2026

  This work proposes a green learning (GL) approach to restore medical images. Without loss of generality, we use low-dose computed tomography (LDCT) images as examples. LDCT images are susceptible to noise and artifacts, where the imaging process introduces distortion. LDCT image restoration is an im…

• Scaling Ultrasound Volumetric Reconstruction via Mobile Augmented Reality

  Kian Wei Ng, Yujia Gao, Deborah Khoo, Ying Zhen Tan, Chengzheng Mao, Haojie Cheng, Andrew Makmur, Kee Yuan Ngiam, Serene Goh, Eng Tat Khoo · 18 de febrero de 2026

  Accurate volumetric characterization of lesions is essential for oncologic diagnosis, risk stratification, and treatment planning. While imaging modalities such as Computed Tomography provide high-quality 3D data, 2D ultrasound (2D-US) remains the preferred first-line modality for breast and thyroid…

• Visualizing the Invisible: Enhancing Radiologist Performance in Breast Mammography via Task-Driven Chromatic Encoding

  Hui Ye, Shilong Yang, Chulong Zhang, Yexuan Xing, Juan Yu, Yaoqin Xie, Wei Zhang · 10 de febrero de 2026

  Purpose:Mammography screening is less sensitive in dense breasts, where tissue overlap and subtle findings increase perceptual difficulty. We present MammoColor, an end-to-end framework with a Task-Driven Chromatic Encoding (TDCE) module that converts single-channel mammograms into TDCE-encoded view…

• Changes in Visual Attention Patterns for Detection Tasks due to Dependencies on Signal and Background Spatial Frequencies

  Amar Kavuri, Howard C. Gifford, Mini Das · 15 de enero de 2026

  We aim to investigate the impact of image and signal properties on visual attention mechanisms during a signal detection task in digital images. The application of insight yielded from this work spans many areas of digital imaging where signal or pattern recognition is involved in complex heterogeno…

• DBT-DINO: Towards Foundation model based analysis of Digital Breast Tomosynthesis

  Felix J. Dorfner, Manon A. Dorster, Ryan Connolly, Oscar Gentilhomme, Edward Gibbs, Steven Graham, Seth Wander, Thomas Schultz, Manisha Bahl, Dania Daye, Albert E. Kim, Christopher P. Bridge · 16 de diciembre de 2025

  Foundation models have shown promise in medical imaging but remain underexplored for three-dimensional imaging modalities. No foundation model currently exists for Digital Breast Tomosynthesis (DBT), despite its use for breast cancer screening. To develop and evaluate a foundation model for DBT (DBT…

• MammoRGB: Dual-View Mammogram Synthesis Using Denoising Diffusion Probabilistic Models

  Jorge Alberto Garza-Abdala, Gerardo A. Fumagal-Gonz\'alez, Daly Avendano, Servando Cardona, Sadam Hussain, Eduardo de Avila-Armenta, Jasiel H. Toscano-Mart\'inez, Diana S. M. Rosales Gurmendi, Alma A. Pedro-P\'erez, Jose Gerardo Tamez-Pena · 1 de diciembre de 2025

  Purpose: This study aims to develop and evaluate a three channel denoising diffusion probabilistic model (DDPM) for synthesizing single breast dual view mammograms and to assess the impact of channel representations on image fidelity and cross view consistency. Materials and Methods: A pretrained th…

• D-PerceptCT: Deep Perceptual Enhancement for Low-Dose CT Images

  Taifour Yousra Nabila, Azeddine Beghdadi, Marie Luong, Zuheng Ming, Habib Zaidi, Faouzi Alaya Cheikh · 19 de noviembre de 2025

  Low Dose Computed Tomography (LDCT) is widely used as an imaging solution to aid diagnosis and other clinical tasks. However, this comes at the price of a deterioration in image quality due to the low dose of radiation used to reduce the risk of secondary cancer development. While some efficient met…

• Toward Better Optimization of Low-Dose CT Enhancement: A Critical Analysis of Loss Functions and Image Quality Assessment Metrics

  Taifour Yousra, Beghdadi Azeddine, Marie Luong, Zuheng Ming · 4 de noviembre de 2025

  Low-dose CT (LDCT) imaging is widely used to reduce radiation exposure to mitigate high exposure side effects, but often suffers from noise and artifacts that affect diagnostic accuracy. To tackle this issue, deep learning models have been developed to enhance LDCT images. Various loss functions hav…

• MV-MLM: Bridging Multi-View Mammography and Language for Breast Cancer Diagnosis and Risk Prediction

  Shunjie-Fabian Zheng, Hyeonjun Lee, Thijs Kooi, Ali Diba · 31 de octubre de 2025

  Large annotated datasets are essential for training robust Computer-Aided Diagnosis (CAD) models for breast cancer detection or risk prediction. However, acquiring such datasets with fine-detailed annotation is both costly and time-consuming. Vision-Language Models (VLMs), such as CLIP, which are pr…