469 citations · 987 across the 21 of their papers we have counts for
23 papers
Towards Real-time Intrahepatic Vessel Identification in Intraoperative Ultrasound-Guided Liver Surgery
Karl-Philippe Beaudet, Alexandros Karargyris, Sidaty El Hadramy +4
While laparoscopic liver resection is less prone to complications and maintains patient outcomes compared to traditional open surgery, its complexity hinders widespread adoption du…
Clinnova Federated Learning Proof of Concept: Key Takeaways from a Cross-border Collaboration
Julia Alekseenko, Bram Stieltjes, Michael Bach +4
Clinnova, a collaborative initiative involving France, Germany, Switzerland, and Luxembourg, is dedicated to unlocking the power of precision medicine through data federation, stan…
Self-supervised Learning via Cluster Distance Prediction for Operating Room Context Awareness
Idris Hamoud, Alexandros Karargyris, Aidean Sharghi +2
Semantic segmentation and activity classification are key components to creating intelligent surgical systems able to understand and assist clinical workflow. In the Operating Room…
BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023
Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu +77
Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%.…
BraTS-Path Challenge: Assessing Heterogeneous Histopathologic Brain Tumor Sub-regions
Spyridon Bakas, Siddhesh P. Thakur, Shahriar Faghani +19
Glioblastoma is the most common primary adult brain tumor, with a grim prognosis - median survival of 12-18 months following treatment, and 4 months otherwise. Glioblastoma is wide…
Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge
Dominic LaBella, Valeriia Abramova, Mehdi Astaraki +102
The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aimed to advance automated segmentation algorithms using the largest known multi-institutional da…
Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge
Dominic LaBella, Ujjwal Baid, Omaditya Khanna +119
We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
Dissecting Self-Supervised Learning Methods for Surgical Computer Vision
Sanat Ramesh, Vinkle Srivastav, Deepak Alapatt +10
The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods. However, standard fu…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases
Hasan Kassem, Deepak Alapatt, Pietro Mascagni +3
Recent advancements in deep learning methods bring computer-assistance a step closer to fulfilling promises of safer surgical procedures. However, the generalizability of such meth…
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Alexandros Karargyris, Renato Umeton, Micah J. Sheller +39
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving prov…
Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Development
Alexandros Karargyris, Satyananda Kashyap, Ismini Lourentzou +8
We developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye tracking system while a radiologist…
Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets
Sandesh Ghimire, Satyananda Kashyap, Joy T. Wu +2
Chest radiography is the most common medical image examination for screening and diagnosis in hospitals. Automatic interpretation of chest X-rays at the level of an entry-level rad…
Looking in the Right place for Anomalies: Explainable AI through Automatic Location Learning
Satyananda Kashyap, Alexandros Karargyris, Joy Wu +5
Deep learning has now become the de facto approach to the recognition of anomalies in medical imaging. Their 'black box' way of classifying medical images into anomaly labels poses…
Chest X-ray Report Generation through Fine-Grained Label Learning
Tanveer Syeda-Mahmood, Ken C. L. Wong, Yaniv Gur +9
Obtaining automated preliminary read reports for common exams such as chest X-rays will expedite clinical workflows and improve operational efficiencies in hospitals. However, the…
Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification
Deepta Rajan, Jayaraman J. Thiagarajan, Alexandros Karargyris +1
Automated diagnostic assistants in healthcare necessitate accurate AI models that can be trained with limited labeled data, can cope with severe class imbalances and can support si…
Distill-to-Label: Weakly Supervised Instance Labeling Using Knowledge Distillation
Jayaraman J. Thiagarajan, Satyananda Kashyap, Alexandros Karagyris
Weakly supervised instance labeling using only image-level labels, in lieu of expensive fine-grained pixel annotations, is crucial in several applications including medical image a…
Boosting the rule-out accuracy of deep disease detection using class weight modifiers
Alexandros Karargyris, Ken C. L. Wong, Joy T. Wu +2
In many screening applications, the primary goal of a radiologist or assisting artificial intelligence is to rule out certain findings. The classifiers built for such applications…
Building a Benchmark Dataset and Classifiers for Sentence-Level Findings in AP Chest X-rays
Tanveer Syeda-Mahmood, Hassan M. Ahmad, Nadeem Ansari +9
Chest X-rays are the most common diagnostic exams in emergency rooms and hospitals. There has been a surge of work on automatic interpretation of chest X-rays using deep learning a…
Age prediction using a large chest X-ray dataset
Alexandros Karargyris, Satyananda Kashyap, Joy T Wu +3
Age prediction based on appearances of different anatomies in medical images has been clinically explored for many decades. In this paper, we used deep learning to predict a person…
Building Disease Detection Algorithms with Very Small Numbers of Positive Samples
Ken C. L. Wong, Alexandros Karargyris, Tanveer Syeda-Mahmood +1
Although deep learning can provide promising results in medical image analysis, the lack of very large annotated datasets confines its full potential. Furthermore, limited positive…
Color Space Transformation Network
Alexandros Karargyris
Deep networks have become very popular over the past few years. The main reason for this widespread use is their excellent ability to learn and predict knowledge in a very easy and…