1 code implementation • 11 Mar 2024 • Ugur Demir, Debesh Jha, Zheyuan Zhang, Elif Keles, Bradley Allen, Aggelos K. Katsaggelos, Ulas Bagci
Deployments of artificial intelligence in medical diagnostics mandate not just accuracy and efficacy but also trust, emphasizing the need for explainability in machine decisions.
no code implementations • 8 Mar 2024 • Xin Zhu, Hongyi Pan, Yury Velichko, Adam B. Murphy, Ashley Ross, Baris Turkbey, Ahmet Enis Cetin, Ulas Bagci
Random samples drawn from latent space are then incorporated with a prototypical corrected image to generate multiple plausible images.
no code implementations • 18 Jan 2024 • Vandan Gorade, Sparsh Mittal, Debesh Jha, Rekha Singhal, Ulas Bagci
This paper presents a novel approach that synergies spatial and spectral representations to enhance domain-generalized medical image segmentation.
1 code implementation • 17 Jan 2024 • Debesh Jha, Nikhil Kumar Tomar, Koushik Biswas, Gorkem Durak, Alpay Medetalibeyoglu, Matthew Antalek, Yury Velichko, Daniela Ladner, Amir Borhani, Ulas Bagci
Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning.
Ranked #5 on Liver Segmentation on LiTS2017
1 code implementation • 28 Dec 2023 • Necip Enes Gengec, Ergin Tari, Ulas Bagci
This study presents an innovative approach for automatic road detection with deep learning, by employing fusion strategies for utilizing both lower-resolution satellite imagery and GPS trajectory data, a concept never explored before.
1 code implementation • 9 Dec 2023 • Quoc-Huy Trinh, Nhat-Tan Bui, Dinh-Hieu Hoang, Phuoc-Thao Vo Thi, Hai-Dang Nguyen, Debesh Jha, Ulas Bagci, Ngan Le, Minh-Triet Tran
Person Re-Identification (Re-ID) task seeks to enhance the tracking of multiple individuals by surveillance cameras.
Clothes Changing Person Re-Identification Person Retrieval +2
no code implementations • 29 Nov 2023 • Koushik Biswas, Debesh Jha, Nikhil Kumar Tomar, Gorkem Durak, Alpay Medetalibeyoglu, Matthew Antalek, Yury Velichko, Daniela Ladner, Amir Bohrani, Ulas Bagci
We apply this new activation function to two important and commonly used general tasks in medical image analysis: automatic disease diagnosis and organ segmentation in CT and MRI.
no code implementations • 28 Nov 2023 • Vandan Gorade, Sparsh Mittal, Debesh Jha, Ulas Bagci
HLFD strategically distills knowledge from a combination of middle layers to earlier layers and transfers final layer knowledge to intermediate layers at both the feature and pixel levels.
1 code implementation • 22 Nov 2023 • Amirhossein Kazerouni, Sanaz Karimijafarbigloo, Reza Azad, Yury Velichko, Ulas Bagci, Dorit Merhof
Semantic segmentation, a crucial task in computer vision, often relies on labor-intensive and costly annotated datasets for training.
1 code implementation • 21 Nov 2023 • Afshin Bozorgpour, Bobby Azad, Reza Azad, Yury Velichko, Ulas Bagci, Dorit Merhof
In addition, we introduce a skeletal loss term to reinforce the model's geometric dependence on the spine.
no code implementations • 21 Nov 2023 • Sanaz Karimijafarbigloo, Reza Azad, Yury Velichko, Ulas Bagci, Dorit Merhof
Current 3D semi-supervised segmentation methods face significant challenges such as limited consideration of contextual information and the inability to generate reliable pseudo-labels for effective unsupervised data use.
1 code implementation • 28 Oct 2023 • Amirhossein Kazerouni, Reza Azad, Alireza Hosseini, Dorit Merhof, Ulas Bagci
INCODE comprises a harmonizer network and a composer network, where the harmonizer network dynamically adjusts key parameters of the activation function.
no code implementations • 26 Oct 2023 • Vandan Gorade, Sparsh Mittal, Debesh Jha, Ulas Bagci
When evaluating skin lesion and brain tumor segmentation datasets, we observe a remarkable improvement of 1. 71% in Intersection-over Union scores for skin lesion segmentation and of 8. 58% for brain tumor segmentation.
no code implementations • 19 Oct 2023 • Zheyuan Zhang, Lanhong Yao, Bin Wang, Debesh Jha, Elif Keles, Alpay Medetalibeyoglu, Ulas Bagci
We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data that preserve the essential characteristics of the original medical images by incorporating edge information of objects to guide the synthesis process.
no code implementations • 2 Oct 2023 • Ariana M. Familiar, Anahita Fathi Kazerooni, Hannah Anderson, Aliaksandr Lubneuski, Karthik Viswanathan, Rocky Breslow, Nastaran Khalili, Sina Bagheri, Debanjan Haldar, Meen Chul Kim, Sherjeel Arif, Rachel Madhogarhia, Thinh Q. Nguyen, Elizabeth A. Frenkel, Zeinab Helili, Jessica Harrison, Keyvan Farahani, Marius George Linguraru, Ulas Bagci, Yury Velichko, Jeffrey Stevens, Sarah Leary, Robert M. Lober, Stephani Campion, Amy A. Smith, Denise Morinigo, Brian Rood, Kimberly Diamond, Ian F. Pollack, Melissa Williams, Arastoo Vossough, Jeffrey B. Ware, Sabine Mueller, Phillip B. Storm, Allison P. Heath, Angela J. Waanders, Jena V. Lilly, Jennifer L. Mason, Adam C. Resnick, Ali Nabavizadeh
Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children.
1 code implementation • 18 Sep 2023 • Hongyi Pan, Bin Wang, Zheyuan Zhang, Xin Zhu, Debesh Jha, Ahmet Enis Cetin, Concetto Spampinato, Ulas Bagci
However, it neglects background interference in the amplitude spectrum.
no code implementations • 11 Sep 2023 • Lanhong Yao, Zheyuan Zhang, Ugur Demir, Elif Keles, Camila Vendrami, Emil Agarunov, Candice Bolan, Ivo Schoots, Marc Bruno, Rajesh Keswani, Frank Miller, Tamas Gonda, Cemal Yazici, Temel Tirkes, Michael Wallace, Concetto Spampinato, Ulas Bagci
We test our proposed decision-fusion model in multi-center data sets of 246 multi-contrast MRI scans and obtain superior performance to the state of the art (SOTA) in this field.
1 code implementation • 31 Aug 2023 • Sanaz Karimijafarbigloo, Reza Azad, Amirhossein Kazerouni, Yury Velichko, Ulas Bagci, Dorit Merhof
Accurate medical image segmentation is of utmost importance for enabling automated clinical decision procedures.
1 code implementation • 31 Aug 2023 • Reza Azad, Amirhossein Kazerouni, Babak Azad, Ehsan Khodapanah Aghdam, Yury Velichko, Ulas Bagci, Dorit Merhof
Vision Transformer (ViT) models have demonstrated a breakthrough in a wide range of computer vision tasks.
1 code implementation • 31 Aug 2023 • Reza Azad, Leon Niggemeier, Michael Huttemann, Amirhossein Kazerouni, Ehsan Khodapanah Aghdam, Yury Velichko, Ulas Bagci, Dorit Merhof
To address these challenges, we introduce the concept of \textbf{Deformable Large Kernel Attention (D-LKA Attention)}, a streamlined attention mechanism employing large convolution kernels to fully appreciate volumetric context.
no code implementations • 7 Aug 2023 • Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci
Our model is designed to perform effectively on out-of-distribution (OOD) datasets from multiple centers.
no code implementations • 31 Jul 2023 • Lanhong Yao, Zheyuan Zhang, Ulas Bagci
Brain tumor segmentation is an active research area due to the difficulty in delineating highly complex shaped and textured tumors as well as the failure of the commonly used U-Net architectures.
1 code implementation • 30 Jul 2023 • Debesh Jha, Vanshali Sharma, Debapriya Banik, Debayan Bhattacharya, Kaushiki Roy, Steven A. Hicks, Nikhil Kumar Tomar, Vajira Thambawita, Adrian Krenzer, Ge-Peng Ji, Sahadev Poudel, George Batchkala, Saruar Alam, Awadelrahman M. A. Ahmed, Quoc-Huy Trinh, Zeshan Khan, Tien-Phat Nguyen, Shruti Shrestha, Sabari Nathan, Jeonghwan Gwak, Ritika K. Jha, Zheyuan Zhang, Alexander Schlaefer, Debotosh Bhattacharjee, M. K. Bhuyan, Pradip K. Das, Deng-Ping Fan, Sravanthi Parsa, Sharib Ali, Michael A. Riegler, Pål Halvorsen, Thomas de Lange, Ulas Bagci
Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps.
1 code implementation • 16 Jul 2023 • Debesh Jha, Vanshali Sharma, Neethi Dasu, Nikhil Kumar Tomar, Steven Hicks, M. K. Bhuyan, Pradip K. Das, Michael A. Riegler, Pål Halvorsen, Ulas Bagci, Thomas de Lange
Integrating real-time artificial intelligence (AI) systems in clinical practices faces challenges such as scalability and acceptance.
1 code implementation • 6 Jul 2023 • Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto, Simone Palazzo, Ulas Bagci, Concetto Spampinato
Generative Adversarial Networks (GANs) have demonstrated their ability to generate synthetic samples that match a target distribution.
no code implementations • 3 Jun 2023 • Debesh Jha, Nikhil Kumar Tomar, Debayan Bhattacharya, Ulas Bagci
We develop a novel real-time deep learning based architecture, TransRUPNet that is based on a Transformer and residual upsampling network for colorectal polyp segmentation to improve OOD generalization.
1 code implementation • 29 May 2023 • Bin Wang, Hongyi Pan, Armstrong Aboah, Zheyuan Zhang, Elif Keles, Drew Torigian, Baris Turkbey, Elizabeth Krupinski, Jayaram Udupa, Ulas Bagci
To our best knowledge, GazeGNN is the first work that adopts GNN to integrate image and eye-gaze data.
no code implementations • 4 May 2023 • Ilkin Isler, Debesh Jha, Curtis Lisle, Justin Rineer, Patrick Kelly, Bulent Aydogan, Mohamed Abazeed, Damla Turgut, Ulas Bagci
In this study, our goal is to show the impact of self-supervised pre-training of transformers for organ at risk (OAR) and tumor segmentation as compared to costly fully-supervised learning.
1 code implementation • 26 Apr 2023 • Bin Wang, Armstrong Aboah, Zheyuan Zhang, Ulas Bagci
This study investigates the potential of eye-tracking technology and the Segment Anything Model (SAM) to design a collaborative human-computer interaction system that automates medical image segmentation.
no code implementations • 23 Apr 2023 • Debesh Jha, Ashish Rauniyar, Abhiskek Srivastava, Desta Haileselassie Hagos, Nikhil Kumar Tomar, Vanshali Sharma, Elif Keles, Zheyuan Zhang, Ugur Demir, Ahmet Topcu, Anis Yazidi, Jan Erik Håakegård, Ulas Bagci
Artificial intelligence (AI) methods hold immense potential to revolutionize numerous medical care by enhancing the experience of medical experts and patients.
no code implementations • 23 Apr 2023 • Smriti Regmi, Aliza Subedi, Ulas Bagci, Debesh Jha
Convolutional neural networks (CNNs) have become the de-facto standard in medical image analysis tasks because of their ability to learn complex features from the available datasets, which makes them surpass humans in many image-understanding tasks.
no code implementations • 13 Apr 2023 • Armstrong Aboah, Ulas Bagci, Abdul Rashid Mussah, Neema Jakisa Owor, Yaw Adu-Gyamfi
Identifying unusual driving behaviors exhibited by drivers during driving is essential for understanding driver behavior and the underlying causes of crashes.
no code implementations • 13 Apr 2023 • Armstrong Aboah, Bin Wang, Ulas Bagci, Yaw Adu-Gyamfi
Real-time implementation of such systems is crucial for traffic surveillance and enforcement, however, most of these systems are not real-time.
1 code implementation • 5 Apr 2023 • Zheyuan Zhang, Bin Wang, Lanhong Yao, Ugur Demir, Debesh Jha, Ismail Baris Turkbey, Boqing Gong, Ulas Bagci
In real-world scenarios, however, it is common for models to encounter data from new and different domains to which they were not exposed to during training.
1 code implementation • 13 Mar 2023 • Debesh Jha, Nikhil Kumar Tomar, Vanshali Sharma, Ulas Bagci
Therefore, we intend to develop a novel real-time deep learning based architecture, Transformer based Residual network (TransNetR), for colon polyp segmentation and evaluate its diagnostic performance.
Ranked #1 on Polyp Segmentation on PolypGen
no code implementations • 5 Feb 2023 • Aliasghar Mortazi, Vedat Cicek, Elif Keles, Ulas Bagci
To this end, we proposed a new cyclic optimization method (\textit{CLMR}) to address the efficiency and accuracy problems in deep learning based medical image segmentation.
no code implementations • 1 Feb 2023 • Elif Keles, Ulas Bagci
We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.
no code implementations • 6 Jan 2023 • Nikhil Kumar Tomar, Ulas Bagci, Debesh Jha
Here, we propose a novel architecture, Residual Upsampling Network (RUPNet) for colon polyp segmentation that can process in real-time and show high recall and precision.
Ranked #45 on Medical Image Segmentation on Kvasir-SEG
1 code implementation • 20 Dec 2022 • Zheyuan Zhang, Bin Wang, Debesh Jha, Ugur Demir, Ulas Bagci
In this regard, style augmentation is a strong DG method taking advantage of instance-specific feature statistics containing informative style characteristics to synthetic novel domains.
no code implementations • 14 Dec 2022 • Tara M. Pattilachan, Ugur Demir, Elif Keles, Debesh Jha, Derk Klatte, Megan Engels, Sanne Hoogenboom, Candice Bolan, Michael Wallace, Ulas Bagci
Current data augmentation techniques and transformations are well suited for improving the size and quality of natural image datasets but are not yet optimized for medical imaging.
no code implementations • 29 Oct 2022 • Abhishek Srivastava, Debesh Jha, Bulent Aydogan, Mohamed E. Abazeed, Ulas Bagci
Head and Neck (H\&N) organ-at-risk (OAR) and tumor segmentations are essential components of radiation therapy planning.
1 code implementation • 24 Oct 2022 • Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci
DilatedSegNet is an encoder-decoder network that uses pre-trained ResNet50 as the encoder from which we extract four levels of feature maps.
no code implementations • 15 Aug 2022 • Abhishek Srivastava, Debesh Jha, Elif Keles, Bulent Aydogan, Mohamed Abazeed, Ulas Bagci
Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning.
no code implementations • 5 Aug 2022 • Ashish Rauniyar, Desta Haileselassie Hagos, Debesh Jha, Jan Erik Håkegård, Ulas Bagci, Danda B. Rawat, Vladimir Vlassov
To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge.
1 code implementation • 19 Jul 2022 • Idil Aytekin, Onat Dalmaz, Kaan Gonc, Haydar Ankishan, Emine U Saritas, Ulas Bagci, Haydar Celik, Tolga Cukur
Monitoring of prevalent airborne diseases such as COVID-19 characteristically involves respiratory assessments.
no code implementations • 6 Jul 2022 • Ismail Irmakci, Zeki Emre Unel, Nazli Ikizler-Cinbis, Ulas Bagci
Based on synthetic image training, our segmentation results were as high as 93. 91\%, 94. 11\%, 91. 63\%, 95. 33\%, for muscle, fat, bone, and bone marrow delineation, respectively.
1 code implementation • 21 Jun 2022 • Federica Proietto Salanitri, Giovanni Bellitto, Simone Palazzo, Ismail Irmakci, Michael B. Wallace, Candice W. Bolan, Megan Engels, Sanne Hoogenboom, Marco Aldinucci, Ulas Bagci, Daniela Giordano, Concetto Spampinato
Early detection of precancerous cysts or neoplasms, i. e., Intraductal Papillary Mucosal Neoplasms (IPMN), in pancreas is a challenging and complex task, and it may lead to a more favourable outcome.
1 code implementation • 17 Jun 2022 • Nikhil Kumar Tomar, Annie Shergill, Brandon Rieders, Ulas Bagci, Debesh Jha
With high efficacy in our performance metrics, we concluded that TransResU-Net could be a strong benchmark for building a real-time polyp detection system for the early diagnosis, treatment, and prevention of colorectal cancer.
1 code implementation • 16 Jun 2022 • Abhishek Srivastava, Nikhil Kumar Tomar, Ulas Bagci, Debesh Jha
We compare our FocalConvNet with other SOTA on Kvasir-Capsule, a large-scale VCE dataset with 44, 228 frames with 13 classes of different anomalies.
2 code implementations • 13 Jun 2022 • Nikhil Kumar Tomar, Abhishek Srivastava, Ulas Bagci, Debesh Jha
The detection and removal of precancerous polyps through colonoscopy is the primary technique for the prevention of colorectal cancer worldwide.
1 code implementation • 1 Jun 2022 • Zheyuan Zhang, Ulas Bagci
Transformer-based neural networks have surpassed promising performance on many biomedical image segmentation tasks due to a better global information modeling from the self-attention mechanism.
1 code implementation • 21 May 2022 • Ugur Demir, Zheyuan Zhang, Bin Wang, Matthew Antalek, Elif Keles, Debesh Jha, Amir Borhani, Daniela Ladner, Ulas Bagci
The premise behind this choice is that the self-attention mechanism of the Transformers allows the network to aggregate the high dimensional feature and provide global information modeling.
1 code implementation • 9 May 2022 • Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci, Sharib Ali
Even though there are deep learning methods developed for this task, variability in polyp size can impact model training, thereby limiting it to the size attribute of the majority of samples in the training dataset that may provide sub-optimal results to differently sized polyps.
no code implementations • 23 Mar 2022 • Amir Emad Marvasti, Ehsan Emad Marvasti, Ulas Bagci
Maximum Probability Framework, powered by Maximum Probability Theorem, is a recent theoretical development in artificial intelligence, aiming to formally define probabilistic models, guiding development of objective functions, and regularization of probabilistic models.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
1 code implementation • 3 Feb 2022 • Ilkin Isler, Curtis Lisle, Justin Rineer, Patrick Kelly, Damla Turgut, Jacob Ricci, Ulas Bagci
Organ at risk (OAR) segmentation is a crucial step for treatment planning and outcome determination in radiotherapy treatments of cancer patients.
1 code implementation • 3 Sep 2021 • Federica Proietto Salanitri, Giovanni Bellitto, Ismail Irmakci, Simone Palazzo, Ulas Bagci, Concetto Spampinato
We propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans.
no code implementations • 11 Apr 2021 • Rodney LaLonde, Naji Khosravan, Ulas Bagci
In this study, we introduce a new family of capsule networks, deformable capsules (DeformCaps), to address a very important problem in computer vision: object detection.
no code implementations • 7 Apr 2021 • Ugur Demir, Ismail Irmakci, Elif Keles, Ahmet Topcu, Ziyue Xu, Concetto Spampinato, Sachin Jambawalikar, Evrim Turkbey, Baris Turkbey, Ulas Bagci
We provide an innovative visual explanation algorithm for general purpose and as an example application, we demonstrate its effectiveness for quantifying lesions in the lungs caused by the Covid-19 with high accuracy and robustness without using dense segmentation labels.
no code implementations • 8 Jan 2021 • Ali Nawaz, Syed Muhammad Anwar, Rehan Liaqat, Javid Iqbal, Ulas Bagci, Muhammad Majid
Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory.
no code implementations • 18 Oct 2020 • Harish RaviPrakash, Syed Muhammad Anwar, Ulas Bagci
We propose a novel capsule network based variational encoder architecture, called Bayesian capsules (B-Caps), to modulate the mean and standard deviation of the sampling distribution in the latent space.
no code implementations • 7 Sep 2020 • Sobia Yousaf, Syed Muhammad Anwar, Harish RaviPrakash, Ulas Bagci
Thus, accurate survival prognosis is an important step in treatment planning.
1 code implementation • 11 May 2020 • Samira Masoudi, Syed M. Anwar, Stephanie A. Harmon, Peter L . Choyke, Baris Turkbey, Ulas Bagci
Abdominal fat quantification is critical since multiple vital organs are located within this region.
2 code implementations • 29 Apr 2020 • Arjun D. Desai, Francesco Caliva, Claudia Iriondo, Naji Khosravan, Aliasghar Mortazi, Sachin Jambawalikar, Drew Torigian, Jutta Ellermann, Mehmet Akcakaya, Ulas Bagci, Radhika Tibrewala, Io Flament, Matthew O`Brien, Sharmila Majumdar, Mathias Perslev, Akshay Pai, Christian Igel, Erik B. Dam, Sibaji Gaj, Mingrui Yang, Kunio Nakamura, Xiaojuan Li, Cem M. Deniz, Vladimir Juras, Ravinder Regatte, Garry E. Gold, Brian A. Hargreaves, Valentina Pedoia, Akshay S. Chaudhari
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression.
no code implementations • 9 Apr 2020 • Rodney LaLonde, Ziyue Xu, Ismail Irmakci, Sanjay Jain, Ulas Bagci
The proposed convolutional-deconvolutional capsule network, SegCaps, shows state-of-the-art results while using a fraction of the parameters of popular segmentation networks.
no code implementations • 1 Mar 2020 • Ismail Irmakci, Syed Muhammad Anwar, Drew A. Torigian, Ulas Bagci
The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and ultrasound) and their precise analysis by expert radiologists.
no code implementations • MIDL 2019 • Samira Masoudi, Stephanie A. Harmon, Stephanie Walker, Sherif Mehralivand, Ulas Bagci, Peter L. Choyke, Baris Turkbey
This work can considerably improve the quality of mpMRI combined assessment for prostate cancer detection.
1 code implementation • 10 Jan 2020 • Rodney LaLonde, Pujan Kandel, Concetto Spampinato, Michael B. Wallace, Ulas Bagci
In this study, we design a novel capsule network architecture (D-Caps) to improve the viability of optical biopsy of colorectal polyps.
no code implementations • 21 Oct 2019 • Amir Emad Marvasti, Ehsan Emad Marvasti, Ulas Bagci, Hassan Foroosh
Instead, the regularizing effects of assuming prior over parameters is seen through maximizing probabilities of models or according to information theory, minimizing the information content of a model.
no code implementations • 16 Oct 2019 • Syed Muhammad Anwar, Tooba Altaf, Khola Rafique, Harish RaviPrakash, Hassan Mohy-ud-Din, Ulas Bagci
Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology.
2 code implementations • 12 Sep 2019 • Rodney LaLonde, Drew Torigian, Ulas Bagci
To the best of our knowledge, this is the first study to investigate capsule networks for making predictions based on radiologist-level interpretable attributes and its applications to medical image diagnosis.
no code implementations • 26 Aug 2019 • Yucheng Liu, Naji Khosravan, Yulin Liu, Joseph Stember, Jonathan Shoag, Christopher E. Barbieri, Ulas Bagci, Sachin Jambawalikar
By using SynCT images (without segmentation labels) and MR images (with segmentation labels available), we have trained a deep segmentation network for precise delineation of prostate from real CT scans.
no code implementations • 18 Aug 2019 • Aliasghar Mortazi, Naji Khosravan, Drew A. Torigian, Sila Kurugol, Ulas Bagci
To alleviate this limitation, in this study, we propose a weakly supervised image segmentation method based on a deep geodesic prior.
no code implementations • 17 Jul 2019 • Sanay Muhammad Umar Saeed, Syed Muhammad Anwar, Humaira Khalid, Muhammad Majid, Ulas Bagci
Stress research is a rapidly emerging area in thefield of electroencephalography (EEG) based signal processing. The use of EEG as an objective measure for cost effective andpersonalized stress management becomes important in particularsituations such as the non-availability of mental health facilities. In this study, long-term stress is classified using baseline EEGsignal recordings.
1 code implementation • 30 Jun 2019 • Rodney LaLonde, Irene Tanner, Katerina Nikiforaki, Georgios Z. Papadakis, Pujan Kandel, Candice W. Bolan, Michael B. Wallace, Ulas Bagci
This is one of the first studies to train an end-to-end deep network on multisequence MRI for IPMN diagnosis, and shows that our proposed novel inflated network architectures are able to handle the extremely limited training data (139 MRI scans), while providing an absolute improvement of $8. 76\%$ in accuracy for diagnosing IPMN over the current state-of-the-art.
no code implementations • 11 Jun 2019 • Naji Khosravan, Aliasghar Mortazi, Michael Wallace, Ulas Bagci
Adversarial learning has been proven to be effective for capturing long-range and high-level label consistencies in semantic segmentation.
no code implementations • 13 May 2019 • Aasim Raheel, Muhammad Majid, Syed Muhammad Anwar, Ulas Bagci
The response to this enhanced multimedia content (mulsemedia) is evaluated in terms of the appreciation/emotion by using human brain signals.
no code implementations • 13 May 2019 • Aamir Arsalan, Muhammad Majid, Syed Muhammad Anwar, Ulas Bagci
In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals.
no code implementations • 8 Apr 2019 • Neslisah Torosdagli, Syed Anwar, Payal Verma, Denise K Liberton, Janice S. Lee, Wade W. Han, Ulas Bagci
Purpose: We perform anatomical landmarking for craniomaxillofacial (CMF) bones without explicitly segmenting them.
no code implementations • 21 Feb 2019 • Xiahai Zhuang, Lei LI, Christian Payer, Darko Stern, Martin Urschler, Mattias P. Heinrich, Julien Oster, Chunliang Wang, Orjan Smedby, Cheng Bian, Xin Yang, Pheng-Ann Heng, Aliasghar Mortazi, Ulas Bagci, Guanyu Yang, Chenchen Sun, Gaetan Galisot, Jean-Yves Ramel, Thierry Brouard, Qianqian Tong, Weixin Si, Xiangyun Liao, Guodong Zeng, Zenglin Shi, Guoyan Zheng, Chengjia Wang, Tom MacGillivray, David Newby, Kawal Rhode, Sebastien Ourselin, Raad Mohiaddin, Jennifer Keegan, David Firmin, Guang Yang
This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017.
no code implementations • 17 Jan 2019 • Samira Masoudi, Afsaneh Razi, Cameron H. G. Wright, Jay C. Gatlin, Ulas Bagci
Our experimental results show that the proposed supervised learning algorithm improves the precision for MT instance velocity estimation drastically to 71. 3% from the baseline result (29. 3%).
2 code implementations • 9 Dec 2018 • Enes Karaaslan, Ulas Bagci, F. Necati Catbas
Conventional methods for visual assessment of civil infrastructures have certain limitations, such as subjectivity of the collected data, long inspection time, and high cost of labor.
no code implementations • 14 Oct 2018 • Ismail Irmakci, Sarfaraz Hussein, Aydogan Savran, Rita R. Kalyani, David Reiter, Chee W. Chia, Kenneth W. Fishbein, Richard G. Spencer, Luigi Ferrucci, Ulas Bagci
Magnetic resonance imaging (MRI) is the non-invasive modality of choice for body tissue composition analysis due to its excellent soft tissue contrast and lack of ionizing radiation.
no code implementations • 6 Oct 2018 • Neslisah Torosdagli, Denise K. Liberton, Payal Verma, Murat Sincan, Janice S. Lee, Ulas Bagci
Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identification of 9 anatomical landmarks of the mandible on the geodesic space.
no code implementations • 19 Jul 2018 • Aliasghar Mortazi, Ulas Bagci
Deep neural network architectures have traditionally been designed and explored with human expertise in a long-lasting trial-and-error process.
no code implementations • 4 Jun 2018 • Nandakishore Puttashamachar, Ulas Bagci
Understanding the structure and organization of the tissues facilitates us with a diagnosis method to identify any aberrations and provide acute information on the occurrences of brain ischemia or stroke, the mutation of neurological diseases such as Alzheimer, multiple sclerosis and so on.
no code implementations • 6 May 2018 • Naji Khosravan, Ulas Bagci
Our approach uses a single feed forward pass of a single network for detection and provides better performance when compared to the current literature.
7 code implementations • 11 Apr 2018 • Rodney LaLonde, Ulas Bagci
A new architecture recently introduced by Sabour et al., referred to as a capsule networks with dynamic routing, has shown great initial results for digit recognition and small image classification.
no code implementations • 17 Feb 2018 • Naji Khosravan, Ulas Bagci
This study set out to test the hypothesis that joint learning of false positive (FP) nodule reduction and nodule segmentation can improve the computer aided diagnosis (CAD) systems' performance on both tasks.
no code implementations • 17 Feb 2018 • Naji Khosravan, Haydar Celik, Baris Turkbey, Elizabeth Jones, Bradford Wood, Ulas Bagci
Computer aided diagnostic (CAD) tools are developed to help radiologists to compensate for some of these errors.
no code implementations • 10 Jan 2018 • Sarfaraz Hussein, Pujan Kandel, Candice W. Bolan, Michael B. Wallace, Ulas Bagci
We evaluate our proposed supervised and unsupervised learning algorithms on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans, respectively, and obtain the state-of-the-art sensitivity and specificity results in both problems.
no code implementations • 26 Oct 2017 • Maria J. M. Chuquicusma, Sarfaraz Hussein, Jeremy Burt, Ulas Bagci
To address this challenge, radiologists need computer aided diagnosis (CAD) systems which can assist in learning discriminative imaging features corresponding to malignant and benign nodules.
no code implementations • 26 Oct 2017 • Sarfaraz Hussein, Pujan Kandel, Juan E. Corral, Candice W. Bolan, Michael B. Wallace, Ulas Bagci
Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically identifiable precursors to pancreatic cancer; hence, early detection and precise risk assessment of IPMN are vital.
no code implementations • 17 Aug 2017 • Dustin Morley, Hassan Foroosh, Saad Shaikh, Ulas Bagci
We propose a new deep learning approach for automatic detection and segmentation of fluid within retinal OCT images.
no code implementations • 3 Aug 2017 • Aliasghar Mortazi, Jeremy Burt, Ulas Bagci
These measurements are derived as outcomes of precise segmentation of the heart and its substructures.
no code implementations • 26 May 2017 • Harish RaviPrakash, Milena Korostenskaja, Eduardo Castillo, Ki Lee, James Baumgartner, Ulas Bagci
In this study, we address the accuracy limitation of the current RTFM signal estimation methods by analyzing the full frequency spectrum of the signal and replacing signal power estimation methods with machine learning algorithms, specifically random forest (RF), as a proof of concept.
no code implementations • 17 May 2017 • Aliasghar Mortazi, Rashed Karim, Kawal Rhode, Jeremy Burt, Ulas Bagci
Anatomical and biophysical modeling of left atrium (LA) and proximal pulmonary veins (PPVs) is important for clinical management of several cardiac diseases.
no code implementations • 28 Apr 2017 • Sarfaraz Hussein, Kunlin Cao, Qi Song, Ulas Bagci
In order to address the need for a large amount for training data for CNN, we resort to transfer learning to obtain highly discriminative features.
no code implementations • 2 Mar 2017 • Sarfaraz Hussein, Robert Gillies, Kunlin Cao, Qi Song, Ulas Bagci
Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning.
no code implementations • 23 Feb 2017 • Neslisah Torosdagli, Denise K. Liberton, Payal Verma, Murat Sincan Janice Lee, Sumanta Pattanaik, Ulas Bagci
Mandible bone segmentation from computed tomography (CT) scans is challenging due to mandible's structural irregularities, complex shape patterns, and lack of contrast in joints.
no code implementations • 21 Sep 2016 • Mario Buty, Ziyue Xu, Mingchen Gao, Ulas Bagci, Aaron Wu, Daniel J. Mollura
Both sets of features were combined to estimate the nodule malignancy using a random forest classifier.
no code implementations • 10 Aug 2016 • Naji Khosravan, Haydar Celik, Baris Turkbey, Ruida Cheng, Evan McCreedy, Matthew McAuliffe, Sandra Bednarova, Elizabeth Jones, Xinjian Chen, Peter L . Choyke, Bradford J. Wood, Ulas Bagci
During diagnostic assessment of lung CT scans, the radiologists' gaze information were used to create a visual attention map.
no code implementations • 15 Dec 2015 • Sarfaraz Hussein, Aileen Green, Arjun Watane, Georgios Papadakis, Medhat Osman, Ulas Bagci
Quantification of adipose tissue (fat) from computed tomography (CT) scans is conducted mostly through manual or semi-automated image segmentation algorithms with limited efficacy.
no code implementations • 11 Jul 2014 • Awais Mansoor, Ulas Bagci, Daniel J. Mollura
Low-resolution and signal-dependent noise distribution in positron emission tomography (PET) images makes denoising process an inevitable step prior to qualitative and quantitative image analysis tasks.
no code implementations • 11 Jul 2014 • Awais Mansoor, Ulas Bagci, Brent Foster, Ziyue Xu, Deborah Douglas, Jeffrey M. Solomon, Jayaram K. Udupa, Daniel J. Mollura
Accurate and fast extraction of lung volumes from computed tomography (CT) scans remains in a great demand in the clinical environment because the available methods fail to provide a generic solution due to wide anatomical variations of lungs and existence of pathologies.
no code implementations • 11 Jul 2014 • Awais Mansoor, Ulas Bagci, Daniel J. Mollura
In this paper, we present a novel approach for fast, accurate, reliable segmentation of pathological lungs from CT scans by combining region-based segmentation method with local descriptor classification that is performed on an optimized sampling grid.
no code implementations • 18 Jul 2009 • Ulas Bagci, Li Bai
In this paper, the problem of automatic Gabor wavelet selection for face recognition is tackled by introducing an automatic algorithm based on Parallel AdaBoosting method.