no code implementations • 26 Feb 2024 • Bidur Khanal, Prashant Shrestha, Sanskar Amgain, Bishesh Khanal, Binod Bhattarai, Cristian A. Linte
Label noise in medical image classification datasets significantly hampers the training of supervised deep learning methods, undermining their generalizability.
no code implementations • 11 Dec 2023 • Prashant Shrestha, Sanskar Amgain, Bidur Khanal, Cristian A. Linte, Binod Bhattarai
Medical Vision Language Pretraining (VLP) has recently emerged as a promising solution to the scarcity of labeled data in the medical domain.
1 code implementation • 8 Aug 2023 • Bidur Khanal, Binod Bhattarai, Bishesh Khanal, Cristian A. Linte
In this work, we explore contrastive and pretext task-based self-supervised pretraining to initialize the weights of a deep learning classification model for two medical datasets with self-induced noisy labels -- NCT-CRC-HE-100K tissue histological images and COVID-QU-Ex chest X-ray images.
1 code implementation • 21 Jun 2023 • Bidur Khanal, Binod Bhattarai, Bishesh Khanal, Danail Stoyanov, Cristian A. Linte
Acquiring properly annotated data is expensive in the medical field as it requires experts, time-consuming protocols, and rigorous validation.
no code implementations • 5 Feb 2023 • Zixin Yang, Richard Simon, Cristian A. Linte
Yet, as a large laparoscopic dataset for training learning-based methods does not exist and the generalization ability of networks remains to be improved, the incorporation of the proposed disparity refinement framework into existing networks will contribute to improving their overall accuracy and robustness associated with depth estimation.
no code implementations • 7 Nov 2022 • Zixin Yang, Richard Simon, Cristian A. Linte
To assist with this task, we explore the use of learning-based feature descriptors, which, to our best knowledge, have not been explored for use in laparoscopic liver registration.
2 code implementations • 16 Sep 2021 • S. M. Kamrul Hasan, Cristian A. Linte
Medical image segmentation has significantly benefitted thanks to deep learning architectures.
no code implementations • 30 Mar 2021 • Roshan Reddy Upendra, Brian Jamison Wentz, Richard Simon, Suzanne M. Shontz, Cristian A. Linte
Patient-specific left ventricle (LV) myocardial models have the potential to be used in a variety of clinical scenarios for improved diagnosis and treatment plans.
1 code implementation • 21 Apr 2020 • S. M. Kamrul Hasan, Cristian A. Linte
In this work, we implement a fully convolutional segmenter featuring both a learned group structure and a regularized weight-pruner to reduce the high computational cost in volumetric image segmentation.
no code implementations • 5 Apr 2020 • S. M. Kamrul Hasan, Cristian A. Linte
With the advent of Cardiac Cine Magnetic Resonance (CMR) Imaging, there has been a paradigm shift in medical technology, thanks to its capability of imaging different structures within the heart without ionizing radiation.
1 code implementation • 24 Feb 2019 • S. M. Kamrul Hasan, Cristian A. Linte
Conventional therapy approaches limit surgeons' dexterity control due to limited field-of-view.
no code implementations • 26 Sep 2018 • Shusil Dangi, Ziv Yaniv, Cristian A. Linte
Segmentation of the left ventricle and quantification of various cardiac contractile functions is crucial for the timely diagnosis and treatment of cardiovascular diseases.
no code implementations • 3 Nov 2016 • Shusil Dangi, Nathan Cahill, Cristian A. Linte
Magnetic Resonance Imaging (MRI) has evolved as a clinical standard-of-care imaging modality for cardiac morphology, function assessment, and guidance of cardiac interventions.