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20222026
most citedFoundation Models for Biomedical Image Segmentation: A Survey

14 citations · 21 across the 12 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis

Naoto Usuyama, Jeya Maria Jose Valanarasu, Sicong Yao +27

Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning trans…

cs.CV2026

BTReport: A Framework for Brain Tumor Radiology Report Generation with Clinically Relevant Features

Juampablo E. Heras Rivera, Dickson T. Chen, Tianyi Ren +4

Recent advances in radiology report generation (RRG) have been driven by large paired image-text datasets; however, progress in neuro-oncology has been limited due to a lack of ope…

cs.CV2025

From Embeddings to Accuracy: Comparing Foundation Models for Radiographic Classification

Xue Li, Jameson Merkow, Noel C. F. Codella +11

Foundation models provide robust embeddings for diverse tasks, including medical imaging. We evaluate embeddings from seven general and medical-specific foundation models (e.g., De…

cs.CV2025

WaveFormer: A 3D Transformer with Wavelet-Driven Feature Representation for Efficient Medical Image Segmentation

Md Mahfuz Al Hasan, Mahdi Zaman, Abdul Jawad +9

Transformer-based architectures have advanced medical image analysis by effectively modeling long-range dependencies, yet they often struggle in 3D settings due to substantial memo…

cs.CV2025

Multi-Modal Mamba Modeling for Survival Prediction (M4Survive): Adapting Joint Foundation Model Representations

Ho Hin Lee, Alberto Santamaria-Pang, Jameson Merkov +2

Accurate survival prediction in oncology requires integrating diverse imaging modalities to capture the complex interplay of tumor biology. Traditional single-modality approaches o…

cs.CV2024★ 14 cited

Foundation Models for Biomedical Image Segmentation: A Survey

Ho Hin Lee, Yu Gu, Theodore Zhao +9

Recent advancements in biomedical image analysis have been significantly driven by the Segment Anything Model (SAM). This transformative technology, originally developed for genera…

cs.CV2023★ 2 cited

3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in Radiology

Asma Ben Abacha, Alberto Santamaria-Pang, Ho Hin Lee +9

The increasing use of medical imaging in healthcare settings presents a significant challenge due to the increasing workload for radiologists, yet it also offers opportunity for en…