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20182024
most citedLooking in the Right place for Anomalies: Explainable AI through Automatic Location Learning

15 citations · 17 across the 7 of their papers we have counts for

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5 papers · 1 filter

eess.IV20241 cited

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…

eess.IV202414 cited

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%.…

eess.IV20249 cited

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…

eess.IV2020

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…

eess.IV2019

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…