Author name cluster

Ulas Bagci

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

ICLR Conference 2025 Conference Paper

Order-aware Interactive Segmentation

Bin Wang 0068
Anwesa Choudhuri
Meng Zheng 0002
Zhongpai Gao
Benjamin Planche
Andong Deng
Qin Liu 0008
Terrence Chen

Interactive segmentation aims to accurately segment target objects with minimal user interactions. However, current methods often fail to accurately separate target objects from the background, due to a limited understanding of order, the relative depth between objects in a scene. To address this issue, we propose OIS: order-aware interactive segmentation, where we explicitly encode the relative depth between objects into order maps. We introduce a novel order-aware attention, where the order maps seamlessly guide the user interactions (in the form of clicks) to attend to the image features. We further present an object-aware attention module to incorporate a strong object-level understanding to better differentiate objects with similar order. Our approach allows both dense and sparse integration of user clicks, enhancing both accuracy and efficiency as compared to prior works. Experimental results demonstrate that OIS achieves state-of-the-art performance, improving mIoU after one click by 7.61 on the HQSeg44K dataset and 1.32 on the DAVIS dataset as compared to the previous state-of-the-art SegNext, while also doubling inference speed compared to current leading methods.

Details

AIIM Journal 2025 Journal Article

Position of artificial intelligence in healthcare and future perspective

Vedat Cicek
Ulas Bagci

Artificial Intelligence (AI) has been used in healthcare with increasing momentum. According to a published report, 6. 6 billion dollars were invested in AI healthcare in 2021, and this investment is expected to provide 150 billion dollars of benefit to the USA economy by 2026 (Duchateau and King, 2023 [1]). The future perspective on AI will undoubtedly open new horizons for the healthcare. AI technology in the healthcare field is increasingly popular in the areas of diagnosis, prognosis, classification, therapy, and disease survival prediction. Now that AI has proven its worth, it's already time to re-ask the following three questions according to the fast pace of AI algorithms: 1) Where will AI be positioned in healthcare in the future? 2) What kind of relationship will be defined between doctors, patients and AI? 3) How can we direct AI studies according to the health problems in the world?

Details DOI

JBHI Journal 2024 Journal Article

COVID-19 Detection From Respiratory Sounds With Hierarchical Spectrogram Transformers

Idil Aytekin
Onat Dalmaz
Kaan Gonc
Haydar Ankishan
Emine Ulku Saritas
Ulas Bagci
Haydar Celik
Tolga Çukur

Monitoring of prevalent airborne diseases such as COVID-19 characteristically involves respiratory assessments. While auscultation is a mainstream method for preliminary screening of disease symptoms, its utility is hampered by the need for dedicated hospital visits. Remote monitoring based on recordings of respiratory sounds on portable devices is a promising alternative, which can assist in early assessment of COVID-19 that primarily affects the lower respiratory tract. In this study, we introduce a novel deep learning approach to distinguish patients with COVID-19 from healthy controls given audio recordings of cough or breathing sounds. The proposed approach leverages a novel hierarchical spectrogram transformer (HST) on spectrogram representations of respiratory sounds. HST embodies self-attention mechanisms over local windows in spectrograms, and window size is progressively grown over model stages to capture local to global context. HST is compared against state-of-the-art conventional and deep-learning baselines. Demonstrations on crowd-sourced multi-national datasets indicate that HST outperforms competing methods, achieving over 90% area under the receiver operating characteristic curve (AUC) in detecting COVID-19 cases.

Details DOI

JBHI Journal 2017 Journal Article

Single-Channel Sparse Non-Negative Blind Source Separation Method for Automatic 3-D Delineation of Lung Tumor in PET Images

Ivica Kopriva
Wei Ju
Bin Zhang
Fei Shi
Dehui Xiang
Kai Yu
Ximing Wang
Ulas Bagci

In this paper, we propose a novel method for single-channel blind separation of nonoverlapped sources and, to the best of our knowledge, apply it for the first time to automatic segmentation of lung tumors in positron emission tomography (PET) images. Our approach first converts a 3-D PET image into a pseudo-multichannel image. Afterward, regularization free sparseness constrained non-negative matrix factorization is used to separate tumor from other tissues. By using complexity based criterion, we select tumor component as the one with minimal complexity. We have compared the proposed method with threshold based on 40% and 50% maximum standardized uptake value (SUV), graph cuts (GC), random walks (RW), and affinity propagation (AP) algorithms on 18 nonsmall cell lung cancer datasets with respect to ground truth (GT) provided by two radiologists. Dice similarity coefficient averaged with respect to two GTs is: 0. 78 ± 0. 12 by the proposed algorithm, 0. 78 ± 0. 1 by GC, 0. 77 ± 0. 13 by AP, 0. 77 ± 0. 07 by RW, and 0. 75 ± 0. 13 by 50% maximum SUV threshold. Since the proposed method achieved performance comparable with interactive methods, considering the unique challenges of lung tumor segmentation from PET images, our findings support possibility of using our fully automated method in routine clinics. The source codes will be available at www. mipav. net/English/research/research. html.

Details DOI