arXiv subjects

Md Sayed Tanveer

Publications and source records attributed to Md Sayed Tanveer.

4 recordsLinked to original sources

arXiv2025

A Computational Perspective on NeuroAI and Synthetic Biological Intelligence

NeuroAI is an emerging field at the intersection of neuroscience and artificial intelligence, where insights from brain function guide the design of intelligent systems. A central area within this field is synthetic biological intelligence (SBI), which combines the adaptive learning properties of biological neural networks with engineered hardware and software. SBI systems provide a platform for modeling neural computation, developing biohybrid architectures, and enabling new forms of embodied intelligence. In this review, we organize the NeuroAI landscape into three interacting domains: hardware, software, and wetware. We outline computational frameworks that integrate biological and non-biological systems and highlight recent advances in organoid intelligence, neuromorphic computing, and neuro-symbolic learning. These developments collectively point toward a new class of systems that compute through interactions between living neural tissue and digital algorithms.

q-bio.NC↗

arXiv2024

Starting a Synthetic Biological Intelligence Lab from Scratch

With the recent advancements in artificial intelligence, researchers and industries are deploying gigantic models trained on billions of samples. While training these models consumes a huge amount of energy, human brains produce similar outputs (along with other capabilities) with massively lower data and energy requirements. For this reason, more researchers are increasingly considering alternatives. One of these alternatives is known as synthetic biological intelligence, which involves training \textit{in vitro} neurons for goal-directed tasks. This multidisciplinary field requires knowledge of tissue engineering, bio-materials, digital signal processing, computer programming, neuroscience, and even artificial intelligence. The multidisciplinary requirements make starting synthetic biological intelligence research highly non-trivial and time-consuming. Generally, most labs either specialize in the biological aspects or the computational ones. Here, we propose how a lab focusing on computational aspects, including machine learning and device interfacing, can start working on synthetic biological intelligence, including organoid intelligence. We will also discuss computational aspects, which can be helpful for labs that focus on biological research. To facilitate synthetic biological intelligence research, we will describe such a general process step by step, including risks and precautions that could lead to substantial delay or additional cost.

q-bio.NC↗

arXiv2024

Development and Validation of a Large Language Model for Generating Fully-Structured Radiology Reports

Current LLMs for creating fully-structured reports face the challenges of formatting errors, content hallucinations, and privacy leakage issues when uploading data to external servers.We aim to develop an open-source, accurate LLM for creating fully-structured and standardized LCS reports from varying free-text reports across institutions and demonstrate its utility in automatic statistical analysis and individual lung nodule retrieval. With IRB approvals, our retrospective study included 5,442 de-identified LDCT LCS radiology reports from two institutions. We constructed two evaluation datasets by labeling 500 pairs of free-text and fully-structured radiology reports and one large-scale consecutive dataset from January 2021 to December 2023. Two radiologists created a standardized template for recording 27 lung nodule features on LCS. We designed a dynamic-template-constrained decoding method to enhance existing LLMs for creating fully-structured reports from free-text radiology reports. Using consecutive structured reports, we automated descriptive statistical analyses and a nodule retrieval prototype. Our best LLM for creating fully-structured reports achieved high performance on cross-institutional datasets with an F1 score of about 97%, with neither formatting errors nor content hallucinations. Our method consistently improved the best open-source LLMs by up to 10.42%, and outperformed GPT-4o by 17.19%. The automatically derived statistical distributions were consistent with prior findings regarding attenuation, location, size, stability, and Lung-RADS. The retrieval system with structured reports allowed flexible nodule-level search and complex statistical analysis. Our developed software is publicly available for local deployment and further research.

cs.AI↗

arXiv2023

X-ray projection imaging of metal oxide particles inside gingival tissues

There is increasing recognition that oral health affects overall health and systemic diseases. Nonetheless it remains challenging to rapidly screen patient biopsies for signs of inflammation or the pathogens or foreign materials that elicit the immune response. This is especially true in conditions such as foreign body gingivitis (FBG), where the foreign particles are often difficult to detect. Our long term goal is to establish a method to determine if the inflammation of the gingival tissue is due to the presence of a metal oxide, with emphasis on elements that were previously reported in FBG biopsies, such as silicon dioxide, silica, and titanium dioxide whose persistent presence can be carcinogenic. In this paper, we proposed to use multiple energy X-ray projection imaging to detect and to differentiate different metal oxide particles embedded inside gingival tissues. To simulate the performance of the imaging system, we have used GATE simulation software to mimic the proposed system and to obtain images with different systematic parameters. The simulated parameters include the X-ray tube anode metal, the X-ray spectra bandwidth, the X-ray focal spot size, the X-ray photon number, and the X-ray dector pixel. We have also applied the de-noising algorithm to obtain better Contrast-to-noise ratio (CNR). Our results indicate that it is feasible to detect metal particles as small as 0.5 micrometer in diameter when we use a Chromium anode target with an energy bandwidth of 5 keV, an X-ray photon number of 10^8, and an X-ray detector with a pixel size of 0.5 micrometer and 100 by 100 pixels. We have also found that different metal particles could be differentiated from the CNR at four different X-ray anodes and spectra. These encouraging initial results will guide our future imaging system design.

physics.med-ph↗