1.2k citations · 2.3k across the 20 of their papers we have counts for
22 papers
MIST: Multimodal Survival Prediction with Genomic-Guided Histology Attention
Muhammet Sami Yavuz, Sabri Mustafa Kahya, Richard R. Chen +2
Multimodal survival models can combine complementary prognostic information from whole-slide images and genomic profiles, but effective fusion remains challenging amid external coh…
Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations
Shubham Innani, Suhang You, Adam Shephard +13
Computational pathology (CompPath) is transforming medicine by leveraging artificial intelligence (AI) algorithms to support diagnosis, prognosis, and treatment prediction from gig…
Training-Free Out-of-Distribution Detection for Pathology Whole-Slide Images
Sabri Mustafa Kahya, Richard R. Chen, Muhammet Sami Yavuz +4
Safe deployment of AI methods in medicine requires robust guardrails that detect when input data deviate from the training distribution to ensure that models provide predictions on…
SHIFT: Survival Prediction from Incomplete and Heterogeneous Genomic Data
Muhammet Sami Yavuz, Ayhan Can Erdur, Sabri Mustafa Kahya +2
Genomic prediction models often fail to transfer across institutions because sequencing panels differ across sites, creating structural feature missingness at deployment. Existing…
A Learnable Prior Improves Inverse Tumor Growth Modeling
Jonas Weidner, Ivan Ezhov, Michal Balcerak +11
Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. H…
Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling
Ivan Ezhov, Kevin Scibilia, Katharina Franitza +12
Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration.…
Algorithm Fairness in AI for Medicine and Healthcare
Richard J. Chen, Tiffany Y. Chen, Jana Lipkova +5
In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care. Rece…
Semi-Implicit Neural Solver for Time-dependent Partial Differential Equations
Suprosanna Shit, Ivan Ezhov, Leon Mächler +6
Fast and accurate solutions of time-dependent partial differential equations (PDEs) are of pivotal interest to many research fields, including physics, engineering, and biology. Ge…
Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning
Richard J. Chen, Ming Y. Lu, Drew F. K. Williamson +8
The rapidly emerging field of deep learning-based computational pathology has demonstrated promise in developing objective prognostic models from histology whole slide images. Howe…
Federated Learning for Computational Pathology on Gigapixel Whole Slide Images
Ming Y. Lu, Dehan Kong, Jana Lipkova +5
Deep Learning-based computational pathology algorithms have demonstrated profound ability to excel in a wide array of tasks that range from characterization of well known morpholog…
Geometry-aware neural solver for fast Bayesian calibration of brain tumor models
Ivan Ezhov, Tudor Mot, Suprosanna Shit +9
Modeling of brain tumor dynamics has the potential to advance therapeutic planning. Current modeling approaches resort to numerical solvers that simulate the tumor progression acco…
Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary
Ming Y. Lu, Melissa Zhao, Maha Shady +4
Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined. This poses a significant challenge since…
Red-GAN: Attacking class imbalance via conditioned generation. Yet another perspective on medical image synthesis for skin lesion dermoscopy and brain tumor MRI
Ahmad B Qasim, Ivan Ezhov, Suprosanna Shit +7
Exploiting learning algorithms under scarce data regimes is a limitation and a reality of the medical imaging field. In an attempt to mitigate the problem, we propose a data augmen…
Implicit Neural Solver for Time-dependent Linear PDEs with Convergence Guarantee
Suprosanna Shit, Abinav Ravi Venkatakrishnan, Ivan Ezhov +3
Fast and accurate solution of time-dependent partial differential equations (PDEs) is of key interest in many research fields including physics, engineering, and biology. Generally…
Neural parameters estimation for brain tumor growth modeling
Ivan Ezhov, Jana Lipkova, Suprosanna Shit +5
Understanding the dynamics of brain tumor progression is essential for optimal treatment planning. Cast in a mathematical formulation, it is typically viewed as evaluation of a sys…
The Liver Tumor Segmentation Benchmark (LiTS)
Patrick Bilic, Patrick Christ, Hongwei Bran Li +106
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedi…
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Spyridon Bakas, Mauricio Reyes, Andras Jakab +421
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritum…
Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans and Bayesian Inference
Jana Lipkova, Panagiotis Angelikopoulos, Stephen Wu +11
Glioblastoma is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltr…
-Leaping: An adaptive, accelerated stochastic simulation algorithm, bridging -leaping and -leaping
Jana Lipková, Georgios Arampatzis, Philippe Chatelain +2
We propose the -leaping algorithm for the acceleration of Gillespie's stochastic simulation algorithm that combines the advantages of the two main accelerated methods; the -l…
Automated Unsupervised Segmentation of Liver Lesions in CT scans via Cahn-Hilliard Phase Separation
Jana Lipková, Markus Rempfler, Patrick Christ +2
The segmentation of liver lesions is crucial for detection, diagnosis and monitoring progression of liver cancer. However, design of accurate automated methods remains challenging…
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
Patrick Ferdinand Christ, Florian Ettlinger, Felix Grün +17
Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision su…
Analysis of Brownian Dynamics Simulations of Reversible Bimolecular Reactions
J. Lipkova, K. C. Zygalakis, S. J. Chapman +1
A class of Brownian dynamics algorithms for stochastic reaction-diffusion models which include reversible bimolecular reactions is presented and analyzed. The method is a generaliz…