54 citations · 123 across the 20 of their papers we have counts for
23 papers
Scaling Artificial Intelligence for Multi-Tumor Early Detection with More Reports, Fewer Masks
Pedro R. A. S. Bassi, Xinze Zhou, Wenxuan Li +20
Early tumor detection save lives. Each year, more than 300 million computed tomography (CT) scans are performed worldwide, offering a vast opportunity for effective cancer screenin…
GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation
Tomasz Szczepański, Szymon Płotka, Michal K. Grzeszczyk +5
Tooth segmentation in Cone-Beam Computed Tomography (CBCT) remains challenging, especially for fine structures like root apices, which is critical for assessing root resorption in…
Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge
Kang Wang, Chen Qin, Zhang Shi +46
Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis. However, the efficacy of these models is highly dependent on…
Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation
Szymon Płotka, Gizem Mert, Maciej Chrabaszcz +2
In recent years, artificial intelligence has significantly advanced medical image segmentation. Nonetheless, challenges remain, including efficient 3D medical image processing acro…
SimuScope: Realistic Endoscopic Synthetic Dataset Generation through Surgical Simulation and Diffusion Models
Sabina Martyniak, Joanna Kaleta, Diego Dall'Alba +3
Computer-assisted surgical (CAS) systems enhance surgical execution and outcomes by providing advanced support to surgeons. These systems often rely on deep learning models trained…
Let Me DeCode You: Decoder Conditioning with Tabular Data
Tomasz Szczepański, Michal K. Grzeszczyk, Szymon Płotka +5
Training deep neural networks for 3D segmentation tasks can be challenging, often requiring efficient and effective strategies to improve model performance. In this study, we intro…
Swin SMT: Global Sequential Modeling in 3D Medical Image Segmentation
Szymon Płotka, Maciej Chrabaszcz, Przemyslaw Biecek
Recent advances in Vision Transformers (ViTs) have significantly enhanced medical image segmentation by facilitating the learning of global relationships. However, these methods fa…
Aggregated Attributions for Explanatory Analysis of 3D Segmentation Models
Maciej Chrabaszcz, Hubert Baniecki, Piotr Komorowski +2
Analysis of 3D segmentation models, especially in the context of medical imaging, is often limited to segmentation performance metrics that overlook the crucial aspect of explainab…
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
Kelly Payette, Céline Steger, Roxane Licandro +64
Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and th…
TabAttention: Learning Attention Conditionally on Tabular Data
Michal K. Grzeszczyk, Szymon Płotka, Beata Rebizant +6
Medical data analysis often combines both imaging and tabular data processing using machine learning algorithms. While previous studies have investigated the impact of attention me…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Multi-task Swin Transformer for Motion Artifacts Classification and Cardiac Magnetic Resonance Image Segmentation
Michal K. Grzeszczyk, Szymon Płotka, Arkadiusz Sitek
Cardiac Magnetic Resonance Imaging is commonly used for the assessment of the cardiac anatomy and function. The delineations of left and right ventricle blood pools and left ventri…
Virtual Reality Simulator for Fetoscopic Spina Bifida Repair Surgery
Przemysław Korzeniowski, Szymon Płotka, Robert Brawura-Biskupski-Samaha +1
Spina Bifida (SB) is a birth defect developed during the early stage of pregnancy in which there is incomplete closing of the spine around the spinal cord. The growing interest in…
BabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video
Szymon Płotka, Michal K. Grzeszczyk, Robert Brawura-Biskupski-Samaha +4
Predicting fetal weight at birth is an important aspect of perinatal care, particularly in the context of antenatal management, which includes the planned timing and the mode of de…
Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings
Sophia Bano, Alessandro Casella, Francisco Vasconcelos +28
Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomos…
Deep Learning Fetal Ultrasound Video Model Match Human Observers in Biometric Measurements
Szymon Płotka, Adam Klasa, Aneta Lisowska +4
Objective. This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, bipar…
CXR-FL: Deep Learning-Based Chest X-ray Image Analysis Using Federated Learning
Filip Ślazyk, Przemysław Jabłecki, Aneta Lisowska +2
Federated learning enables building a shared model from multicentre data while storing the training data locally for privacy. In this paper, we present an evaluation (called CXR-FL…
POTHER: Patch-Voted Deep Learning-Based Chest X-ray Bias Analysis for COVID-19 Detection
Tomasz Szczepański, Arkadiusz Sitek, Tomasz Trzciński +1
A critical step in the fight against COVID-19, which continues to have a catastrophic impact on peoples lives, is the effective screening of patients presented in the clinics with…
FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements
Szymon Płotka, Tomasz Włodarczyk, Adam Klasa +3
In this paper, we propose an end-to-end multi-task neural network called FetalNet with an attention mechanism and stacked module for spatio-temporal fetal ultrasound scan video ana…
Convolutional Neural Networks in Orthodontics: a review
Szymon Płotka, Tomasz Włodarczyk, Ryszard Szczerba +5
Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis. This rev…
Spontaneous preterm birth prediction using convolutional neural networks
Tomasz Włodarczyk, Szymon Płotka, Przemysław Rokita +4
An estimated 15 million babies are born too early every year. Approximately 1 million children die each year due to complications of preterm birth (PTB). Many survivors face a life…
Estimation of preterm birth markers with U-Net segmentation network
Tomasz Włodarczyk, Szymon Płotka, Tomasz Trzciński +4
Preterm birth is the most common cause of neonatal death. Current diagnostic methods that assess the risk of preterm birth involve the collection of maternal characteristics and tr…