arXiv subjects

Zhiling Yue

Publications and source records attributed to Zhiling Yue.

4 recordsLinked to original sources

arXiv2024

Cyclic Vision-Language Manipulator: Towards Reliable and Fine-Grained Image Interpretation for Automated Report Generation

Despite significant advancements in automated report generation, the opaqueness of text interpretability continues to cast doubt on the reliability of the content produced. This paper introduces a novel approach to identify specific image features in X-ray images that influence the outputs of report generation models. Specifically, we propose Cyclic Vision-Language Manipulator CVLM, a module to generate a manipulated X-ray from an original X-ray and its report from a designated report generator. The essence of CVLM is that cycling manipulated X-rays to the report generator produces altered reports aligned with the alterations pre-injected into the reports for X-ray generation, achieving the term "cyclic manipulation". This process allows direct comparison between original and manipulated X-rays, clarifying the critical image features driving changes in reports and enabling model users to assess the reliability of the generated texts. Empirical evaluations demonstrate that CVLM can identify more precise and reliable features compared to existing explanation methods, significantly enhancing the transparency and applicability of AI-generated reports.

cs.CV↗

arXiv2024

Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generation

Fibrotic Lung Disease (FLD) is a severe condition marked by lung stiffening and scarring, leading to respiratory decline. High-resolution computed tomography (HRCT) is critical for diagnosing and monitoring FLD; however, fibrosis appears as irregular, diffuse patterns with unclear boundaries, leading to high inter-observer variability and time-intensive manual annotation. To tackle this challenge, we propose DiffSeg, a novel weakly supervised semantic segmentation (WSSS) method that uses image-level annotations to generate pixel-level fibrosis segmentation, reducing the need for fine-grained manual labeling. Additionally, our DiffSeg incorporates a diffusion-based generative model to synthesize HRCT images with different levels of fibrosis from healthy slices, enabling the generation of the fibrosis-injected slices and their paired fibrosis location. Experiments indicate that our method significantly improves the accuracy of pseudo masks generated by existing WSSS methods, greatly reducing the complexity of manual labeling and enhancing the consistency of the generated masks.

eess.IV↗

arXiv2023

Age of Information in Locally Adaptive Frame Slotted ALOHA

We consider a random access network consisting of source-destination pairs. Each source node generates status updates and transmits this information to its intended destination over a shared spectrum. The goal is to minimize the network-wide Age of Information (AoI). We develop a frame slotted ALOHA (FSA)-based policy for generating and transmitting status updates, where the frame size of each source node is adjusted according to its local environment. The proposed policy is of low complexity and can be implemented in a distributed manner. Additionally, it significantly improves the network AoI performance by (a) equalizing the update generation intervals at each source and (b) reducing interference across the network. Furthermore, we derive an analytical expression for the average network AoI attained for that policy. We evaluate the performance of the proposed scheme through simulations, which demonstrate that the locally adaptive FSA policy achieves a remarkable gain in terms of AoI compared to the slotted ALOHA counterpart, confirming the effectiveness of the proposed method.

cs.NI↗

arXiv2023

Age of Information Under Frame Slotted ALOHA-Based Status Updating Protocol

We propose a frame slotted ALOHA (FSA)-based protocol for a random access network where sources transmit status updates to their intended destinations. We evaluate the effect of such a protocol on the network's timeliness performance using the Age of Information (AoI) metric. Specifically, we leverage tools from stochastic geometry to model the spatial positions of the source-destination pairs and capture the entanglement amongst the nodes' spatial-temporal attributes through the interference they caused to each other. We derive analytical expressions for the average and variance of AoI over a typical transmission link in Poisson bipolar and cellular networks, respectively. Our analysis shows that in densely deployed networks, the FSA-based status updating protocol can significantly decrease the average AoI and in addition, stabilizes the age performance by substantially reducing the variance of AoI. Furthermore, under the same updating frequency, converting a slotted ALOHA protocol into an FSA-based one always leads to a reduction in the average AoI. Moreover, implementing FSA in conjunction with power control can further benefit the AoI performance, although the particular values of framesize and power control factor must be adequately tuned to achieve the optimal gain.

cs.IT↗