arXiv:2607. 10188v1 Announce Type: cross Abstract: Breast cancer remains the most commonly diagnosed malignancy among women worldwide, yet accurate detection and characterization of breast masses in mammography remain challenging due to subtle intensity variations, heterogeneous tissue densities, and indistinct lesion boundaries that complicate radiological interpretation.
By Abu Fatema Mohammad Abdun Noor, Md Imam Ahasan, Md Samiul Ahasan, Kah Ong Michael Goh, S M Hasan Mahmud, Raihana Zannat
arXiv:2608. 10271v1 Announce Type: cross Abstract: Breast density classification is a critical component of breast cancer risk assessment, yet AI models often struggle to generalize across clinical sites due to vendor-specific acquisition styles.
By Hongyi Pan, Gorkem Durak, Halil Ertugrul Aktas, Andrea Mia Bejar, Mustafa Ege Seker, Nebile Alibeyoglu, Rumeysa Guclu, Rana Gunoz Comert Bozkurt, Sibel Ozkan Gurdal, Neslihan Cabioglu, Beyza Ozcinar, Ravza Yilmaz, Vahit Ozmen, Erkin Aribal, Sukru Mehmet Erturk, Yalda Zafari, Mohamed Mabrok, Kayhan Batmanghelich, Mohammad Yaqub, Ziyue Xu, Ulas Bagci
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.
By Istiak Ahmed, Galib Ahmed, K. Shahriar Sanjid, Md. Tanzim Hossain, Md. Nishan Khan, Md. Misbah Khan, Md. Arifur Rahman, Sheikh Anisul Haque, Sharmin Akhtar Rupa, Mohammed Mejbahuddin Mia, Mahmud Hasan Mostofa Kamal, Md. Mostafa Kamal Sarker, M. Monir Uddin
arXiv:2610.35704v1 Announce Type: new
Abstract: Recently, there has been rise in multimodal vision-language models for computer-aided diagnosis (CAD), yet their application in breast diagnosis, parti...
By Sadam Hussain, Mansoor Ali, Daly Betzabeth Avenda\~no-Avalos, Jose Gerardo Tamez-Pe\~na
The paper presents a lightweight CNN‑integrated Compact Convolutional Transformer (CCT) designed for multi‑scale feature learning in breast cancer mammography. With only 250,435 parameters, the model achieved 99‑100% accuracy across three datasets using 5‑fold cross‑validation, demonstrating robust generalization. Explainable AI components were added to clarify the classification process, aiming to increase clinical trust in resource‑constrained settings.
By Md Taimur Ahad (Department of Management North South University, Dhaka, Bangladesh), Ainuddin Ahmed (Department of Management North South University, Dhaka, Bangladesh)
The study evaluates ten ImageNet‑pretrained deep learning models for classifying benign versus malignant breast cancer in the BreakHis histopathology dataset, using a patient‑level split. DenseNet121 achieved the highest performance with 99.4% accuracy and an AUC‑ROC of 0.999, outperforming all other models (p < 0.001). Explainability methods (Grad‑CAM, LIME, Integrated Gradients, LRP) consistently identified nuclei‑rich and glandular regions as key diagnostic features, demonstrating that transfer learning combined with explainable AI can produce accurate and interpretable breast cancer diagnostic tools.
By Sami Alnawali, Muhammad Atif Saeed, Abdul Hadi, Azka Atiq, Akhtar Jamil, Ahmad Din
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...
By Farnoush Bayatmakou, Maryam Hosseini, Reza Taleei, Arash Mohammadi
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:2608. 15019v1 Announce Type: cross Abstract: Breast mass segmentation is an important step in computer-aided mammography, but it remains difficult because masses can have low contrast, irregular shapes, and boundaries that blend with surrounding breast tissue.
By Alibek Kamiluly, Milana Muratova, Yash Patel, Fan Li
arXiv:2607. 06309v1 Announce Type: cross Abstract: Accurate breast cancer classification from mammography requires effective integration of complementary information from craniocaudal (CC) and mediolateral oblique (MLO) views, which provide a more complete characterization of breast abnormalities.
By Aysan Ghayouri Pirsoltan, Shima Babakordi, Mohammad Reza Mohammadi
Accurate breast cancer classification from mammography requires effective integration of complementary information from craniocaudal (CC) and mediolateral oblique (MLO) views, which provide a more complete characterization of breast abnormalities. However, existing multi-view learning approaches typically rely on feature-level aggregation or single-stage cross-attention, which can entangle view-specific and shared representations and restrict interaction to limited network depths.
The paper introduces a Hybrid Cross-Modal Attention Network (HCMAN) that fuses mammogram images with structured clinical data using transformer-based cross‑modal attention. Trained on a locally collected dataset of 2,560 images from 1,024 Ethiopian patients, the model achieves 97.8% accuracy, 97.2% sensitivity, 98.3% specificity, and an AUC of 0.987, outperforming image‑only baselines and maintaining robustness to low‑quality images. Its lightweight design allows inference in under two seconds on a standard CPU, making it suitable for deployment in resource‑limited clinical settings.
By Simon Hadush Nrea (Mekelle University, Mekelle, Ethiopia), Filimon Gidey Gebremichael (Mekelle University, Mekelle, Ethiopia), Gebrekirstos Hagos Gebrekirstos (Clinical Oncologist London School of Hygiene and Tropical Medicine London, UK), Yaecob Girmay Gezahegn (Mekelle University, Mekelle, Ethiopia)