24 citations · 26 across the 12 of their papers we have counts for
13 papers
Dino-NestedUNet: Unlocking Foundation Vision Encoders for Pathology Tumor Bulk Segmentation via Dense Decoding
Tianyang Wang, Ziyu Su, Abdul Rehman Akbar +7
Vision foundation models (VFMs), such as DINOv3, provide rich semantic representations that are promising for computational pathology. However, many current adaptations pair frozen…
Unified Multi-Foundation-Model Slide Representation for Pan-Cancer Recognition and Text-Guided Tumor Localization
Tianyang Wang, Ziyu Su, Abdul Rehman Akbar +6
The expanding ecosystem of pathology foundation models has produced powerful but fragmented tile-level representations, limiting their use in clinical tasks that require unified sl…
RANGER: Sparsely-Gated Mixture-of-Experts with Adaptive Retrieval Re-ranking for Pathology Report Generation
Yixin Chen, Ziyu Su, Hikmat Khan +1
Pathology report generation remains a relatively under-explored downstream task, primarily due to the gigapixel scale and complex morphological heterogeneity of Whole Slide Images…
HistoMet: A Pan-Cancer Deep Learning Framework for Prognostic Prediction of Metastatic Progression and Site Tropism from Primary Tumor Histopathology
Yixin Chen, Ziyu Su, Lingbin Meng +4
Metastatic Progression remains the leading cause of cancer-related mortality, yet predicting whether a primary tumor will metastasize and where it will disseminate directly from hi…
Progressive Translation of H&E to IHC with Enhanced Structural Fidelity
Yuhang Kang, Ziyu Su, Tianyang Wang +3
Compared to hematoxylin-eosin (H&E) staining, immunohistochemistry (IHC) not only maintains the structural features of tissue samples, but also provides high-resolution protein loc…
Hyperparameter Optimization and Reproducibility in Deep Learning Model Training
Usman Afzaal, Ziyu Su, Usama Sajjad +4
Reproducibility remains a critical challenge in foundation model training for histopathology, often hindered by software randomness, hardware non-determinism, and inconsistent hype…
Morphology-Aware Prognostic model for Five-Year Survival Prediction in Colorectal Cancer from H&E Whole Slide Images
Usama Sajjad, Abdul Rehman Akbar, Ziyu Su +5
Colorectal cancer (CRC) remains the third most prevalent malignancy globally, with approximately 154,000 new cases and 54,000 projected deaths anticipated for 2025. The recent adva…
Streamline pathology foundation model by cross-magnification distillation
Ziyu Su, Abdul Rehman Akbar, Usama Sajjad +2
Foundation models (FM) have transformed computational pathology but remain computationally prohibitive for clinical deployment due to their massive parameter counts and high-magnif…
Learning the Language of Histopathology Images reveals Prognostic Subgroups in Invasive Lung Adenocarcinoma Patients
Abdul Rehman Akbar, Usama Sajjad, Ziyu Su +5
Recurrence remains a major clinical challenge in surgically resected invasive lung adenocarcinoma, where existing grading and staging systems fail to capture the cellular complexit…
Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images
Hikmat Khan, Ziyu Su, Huina Zhang +6
Triple-negative breast cancer (TNBC) remains a major clinical challenge due to its aggressive behavior and lack of targeted therapies. Accurate early prediction of response to neoa…
Computational Pathology for Accurate Prediction of Breast Cancer Recurrence: Development and Validation of a Deep Learning-based Tool
Ziyu Su, Yongxin Guo, Robert Wesolowski +4
Accurate recurrence risk stratification is crucial for optimizing treatment plans for breast cancer patients. Current prognostic tools like Oncotype DX (ODX) offer valuable genomic…
Cross-attention-based saliency inference for predicting cancer metastasis on whole slide images
Ziyu Su, Mostafa Rezapour, Usama Sajjad +3
Although multiple instance learning (MIL) methods are widely used for automatic tumor detection on whole slide images (WSI), they suffer from the extreme class imbalance within the…
Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images
Ziyu Su, Mostafa Rezapour, Usama Sajjad +2
Multiple instance learning (MIL) models have achieved remarkable success in analyzing whole slide images (WSIs) for disease classification problems. However, with regard to gigapix…