25 citations · 59 across the 15 of their papers we have counts for
16 papers
NucEval: A Robust Evaluation Framework for Nuclear Instance Segmentation
Amirreza Mahbod, Ramona Woitek, Jeanne Shen
In computational pathology, nuclear instance segmentation is a fundamental task with many downstream clinical applications. With the advent of deep learning, many approaches, inclu…
Effect of Patch Size on Fine-Tuning Vision Transformers in Two-Dimensional and Three-Dimensional Medical Image Classification
Massoud Dehghan, Ramona Woitek, Amirreza Mahbod
Vision Transformers (ViTs) and their variants have become state-of-the-art in many computer vision tasks and are widely used as backbones in large-scale vision and vision-language…
NucFuseRank: Dataset Fusion and Performance Ranking for Nuclei Instance Segmentation
Nima Torbati, Anastasia Meshcheryakova, Ramona Woitek +3
Nuclei instance segmentation in hematoxylin and eosin (H&E)-stained images plays an important role in automated histological image analysis, with various applications in downstream…
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…
ACS-SegNet: An Attention-Based CNN-SegFormer Segmentation Network for Tissue Segmentation in Histopathology
Nima Torbati, Anastasia Meshcheryakova, Ramona Woitek +2
Automated histopathological image analysis plays a vital role in computer-aided diagnosis of various diseases. Among developed algorithms, deep learning-based approaches have demon…
Fusion of Foundation and Vision Transformer Model Features for Dermatoscopic Image Classification
Amirreza Mahbod, Rupert Ecker, Ramona Woitek
Accurate classification of skin lesions from dermatoscopic images is essential for diagnosis and treatment of skin cancer. In this study, we investigate the utility of a dermatolog…
Improved tissue sodium concentration quantification in breast cancer by reducing partial volume effects: a preliminary study
Olgica Zaric, Carmen Leser, Vladimir Juras +10
Introduction: In sodium (23Na) magnetic resonance imaging (MRI), partial volume effects (PVE) are one of the most common causes of errors in the in vivo quantification of tissue so…
Variational U-Net with Local Alignment for Joint Tumor Extraction and Registration (VALOR-Net) of Breast MRI Data Acquired at Two Different Field Strengths
Muhammad Shahkar Khan, Haider Ali, Laura Villazan Garcia +5
Background: Multiparametric breast MRI data might improve tumor diagnostics, characterization, and treatment planning. Accurate alignment and delineation of images acquired at diff…
Evaluating Pre-trained Convolutional Neural Networks and Foundation Models as Feature Extractors for Content-based Medical Image Retrieval
Amirreza Mahbod, Nematollah Saeidi, Sepideh Hatamikia +1
Medical image retrieval refers to the task of finding similar images for given query images in a database, with applications such as diagnosis support. While traditional medical im…
WCEbleedGen: A wireless capsule endoscopy dataset and its benchmarking for automatic bleeding classification, detection, and segmentation
Palak Handa, Manas Dhir, Amirreza Mahbod +4
Computer-based analysis of Wireless Capsule Endoscopy (WCE) is crucial. However, a medically annotated WCE dataset for training and evaluation of automatic classification, detectio…
Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy
Palak Handa, Amirreza Mahbod, Florian Schwarzhans +11
We present the Capsule Vision 2024 Challenge: Multi-Class Abnormality Classification for Video Capsule Endoscopy. It was virtually organized by the Research Center for Medical Imag…
Intensity Normalization Techniques and Their Effect on the Robustness and Predictive Power of Breast MRI Radiomics
Florian Schwarzhans, Geevarghese George, Lorena Escudero Sanchez +4
Radiomics analysis has emerged as a promising approach for extracting quantitative features from medical images to aid in cancer diagnosis and treatment. However, radiomics researc…
Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images
Nematollah Saeidi, Hossein Karshenas, Bijan Shoushtarian +3
Breast cancer is the most common cancer type in women worldwide. Early detection and appropriate treatment can significantly reduce its impact. While histopathology examinations pl…
Breast MRI radiomics and machine learning radiomics-based predictions of response to neoadjuvant chemotherapy -- how are they affected by variations in tumour delineation?
Sepideh Hatamikia, Geevarghese George, Florian Schwarzhans +2
Manual delineation of volumes of interest (VOIs) by experts is considered the gold-standard method in radiomics analysis. However, it suffers from inter- and intra-operator variabi…
Improving Generalization Capability of Deep Learning-Based Nuclei Instance Segmentation by Non-deterministic Train Time and Deterministic Test Time Stain Normalization
Amirreza Mahbod, Georg Dorffner, Isabella Ellinger +2
With the advent of digital pathology and microscopic systems that can scan and save whole slide histological images automatically, there is a growing trend to use computerized meth…
NuInsSeg: A Fully Annotated Dataset for Nuclei Instance Segmentation in H&E-Stained Histological Images
Amirreza Mahbod, Christine Polak, Katharina Feldmann +6
In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for…