257 citations · 284 across the 15 of their papers we have counts for
18 papers
PlanPath: Exploration-Guided Orchestration for Patient-Level Pathology Reasoning
Jiusong Ge, Mingzhao He, Qidong Liu +8
A patient's pathology case spans tens of slides from distinct specimens and anatomical sites, each carrying non-redundant, site-specific evidence. Forming a patient-level conclusio…
Modeling Whole-Slide Images as Dynamic Tumor Microenvironment Fields
Lei Wu, Jiashuai Liu, Di Zhang +9
Due to the gigapixel-scale nature of whole-slide images (WSIs), weakly supervised WSI analysis is commonly formulated as a multiple instance learning (MIL) problem, where patch-lev…
Can Protein-Derived Knowledge Improve Pathology Foundation Models?
Di Zhang, Zhangpeng Gong, Jiashuai Liu +10
Molecularly guided pathology foundation models (PFMs) exploit transcriptomic or proteomic information to enrich whole-slide image (WSI) representations, yet effectively leveraging…
AGE-MIL: Anchor-Guided Evidence Learning for Patient-Level Prediction
Jiawei Niu, Jian Chen, Di Zhang +8
Existing computational pathology methods predominantly operate within whole-slide image (WSI)-level multiple instance learning (MIL) paradigms, while patient-level modeling remains…
Thinking in Scales: Accelerating Gigapixel Pathology Image Analysis via Adaptive Continuous Reasoning
Jiusong Ge, Yingkang Zhan, Wenjie Zhao +13
Traditional whole slide image (WSI) analysis methods typically rely on the multiple instance learning (MIL) paradigm, which extracts patch-level features at high magnification and…
Vision Transformers for Preoperative CT-Based Prediction of Histopathologic Chemotherapy Response Score in High-Grade Serous Ovarian Carcinoma
Francesca Fati, Felipe Coutinho, Marika Reinius +25
Purpose. High-grade serous ovarian carcinoma (HGSOC) is characterized by pronounced biological and spatial heterogeneity and is frequently diagnosed at an advanced stage. Neoadjuva…
CARE: A Molecular-Guided Foundation Model with Adaptive Region Modeling for Whole Slide Image Analysis
Di Zhang, Zhangpeng Gong, Xiaobo Pang +14
Foundation models have recently achieved impressive success in computational pathology, demonstrating strong generalization across diverse histopathology tasks. However, existing m…
Data-driven registration and modeling of brain deformation for image-guided neurosurgery
Tiago Assis, Colin P. Galvin, Joshua P. Castillo +12
Accurate compensation of brain deformation is critical for reliable image-guided neurosurgery. Surgical manipulation and tumor resection induce tissue motion, causing preoperative…
Developing Predictive and Robust Radiomics Models for Chemotherapy Response in High-Grade Serous Ovarian Carcinoma
Sepideh Hatamikia, Geevarghese George, Florian Schwarzhans +10
Objectives: High-grade serous ovarian carcinoma (HGSOC) is typically diagnosed at an advanced stage with extensive peritoneal metastases, making treatment challenging. Neoadjuvant…
RSeg: Training-Free OOD Medical Tumor Segmentation via Anatomical Reasoning and Statistical Rejection
Shuaike Shen, Ke Liu, Jiaqing Xie +5
Foundation models for medical image segmentation struggle under out-of-distribution (OOD) shifts, often producing fragmented false positives on OOD tumors. We introduce RSeg,…
Evaluating Foundation Models with Pathological Concept Learning for Kidney Cancer
Shangqi Gao, Sihan Wang, Yibo Gao +6
To evaluate the translational capabilities of foundation models, we develop a pathological concept learning approach focused on kidney cancer. By leveraging TNM staging guidelines…
Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer
Daniël Boeke, Cedrik Blommestijn, Rebecca N. Wray +8
Recurrence risk estimation in clear cell renal cell carcinoma (ccRCC) is essential for guiding postoperative surveillance and treatment. The Leibovich score remains widely used for…
PH2ST:ST-Prompt Guided Histological Hypergraph Learning for Spatial Gene Expression Prediction
Yi Niu, Jiashuai Liu, Yingkang Zhan +8
Spatial Transcriptomics (ST) reveals the spatial distribution of gene expression in tissues, offering critical insights into biological processes and disease mechanisms. However, t…
CTARR: A fast and robust method for identifying anatomical regions on CT images via atlas registration
Thomas Buddenkotte, Roland Opfer, Julia Krüger +2
Medical image analysis tasks often focus on regions or structures located in a particular location within the patient's body. Often large parts of the image may not be of interest…
CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival Analysis
William Knottenbelt, William McGough, Rebecca Wray +5
Motivation: Survival analysis is a branch of statistics that is crucial in medicine for modeling the time to critical events such as death or relapse, in order to improve treatment…
Automated Small Kidney Cancer Detection in Non-Contrast Computed Tomography
William McGough, Thomas Buddenkotte, Stephan Ursprung +3
This study introduces an automated pipeline for renal cancer (RC) detection in non-contrast computed tomography (NCCT). In the development of our pipeline, we test three detections…
Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation
Thomas Buddenkotte, Lorena Escudero Sanchez, Mireia Crispin-Ortuzar +6
Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only develop…
Toward Early-Warning Detection of Gravitational Waves from Compact Binary Coalescence
Kipp Cannon, Romain Cariou, Adrian Chapman +11
Rapid detection of compact binary coalescence (CBC) with a network of advanced gravitational-wave detectors will offer a unique opportunity for multi-messenger astronomy. Prompt de…