Unifying Research Paper IEEE standard : Multimodal Diagnostic Agent
Budget: ₹600 – ₹1,500 INR
Project Overview: I am looking for a freelancer to draft a base research paper that consolidates concepts from a specific project (Causal Multimodal Diagnostic Agent) and several reference IEEE papers. The goal is to create a unified paper that synthesizes the observations, methodologies, and results from the provided materials into a single cohesive document.
What Will Be Provided:
Main Project Details: Documentation/summary of the "Causal Multimodal Diagnostic Agent" project.
Reference Papers: A list of IEEE-standard papers related to the topic.
Scope of Work: You are required to:
Review: Read the provided project details and the additional reference papers.
Synthesize: Combine the observations, methods, and findings from all provided sources.
Draft: Write a structured research paper (Abstract, Introduction, Literature Review, Methodology, Conclusion) that serves as the "base paper" for this project.
Format: The paper must follow standard research paper formatting (IEEE style preferred).
Strict Constraints:
Budget: ₹500 INR (Fixed). This is for a base paper draft. Please do not bid if you cannot work within this budget.
Deadline: December 26th. This is a strict deadline. Late submissions will not be accepted.
Payment Terms & Revision Policy:
Payment Condition: Payment will only be released after the final draft is approved.
Revisions: You must be available to make specific updates or corrections based on my feedback.
How to Apply: Please confirm you have read the budget and deadline constraints and are comfortable with the payment terms (release upon successful updates).
below are the paper reference to be strictly :
[1] Z. Yao et al., "Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis," in Proc. 2024 IEEE 2nd Int. Conf. Sens., Electron. Comput. Eng. (ICSECE), May 2024, pp. 1-6. doi: 10.1109/ICSECE61636.2024.10729527.
[2] N. Aksoy, S. Sharoff, S. Baser, N. Ravikumar, and A. F. Frangi, "Beyond images: an integrative multi-modal approach to chest xray report generation," Front. Radiol., vol. 4, p. 1339612, Feb. 2024. doi: 10.3389/fradi.2024.1339612.
[3] C. Pellegrini, M. Keicher, E. Özsoy, P. Jiraskova, R. Braren, and N. Navab, "Xplainer: From X-Ray Observations to Explainable Zero-Shot Diagnosis," in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, vol. 14224, Cham: Springer Nature Switzerland, 2023, pp. 420–429. doi: 10.1007/978-3-031-43904-9_41.
[4] A. Lucieri et al., "ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions," Comput. Methods Programs Biomed., vol. 215, p. 106620, Mar. 2022. doi: 10.1016/j.cmpb.2022.106620.
[5] J. Sun, L. Wang, and Y. Zheng, "Lesion Guided Explainable Few Weak-Shot Medical Report Generation," in Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, vol. 13435, Cham: Springer Nature Switzerland, 2022, pp. 615–625. doi: 10.1007/978-3-031-16443-9_59.
[6] Q. Han, J. Liu, Z. Qin, and Z. Zheng, "Integrating MedCLIP and Cross-Modal Fusion for Automatic Radiology Report Generation," in Proc. 2024 IEEE Int. Conf. Big Data (BigData), Dec. 2024, pp. 7313-7317. doi: 10.1109/bigdata62323.2024.10825240.
[7] H. Alam, D. Srivastav, and M. A. Kadir, "Towards Interpretable Radiology Report Generation via Concept Bottlenecks using a Multi-Agentic RAG," in Advances in Information Retrieval, vol. 15574, Cham: Springer Nature Switzerland, 2025, pp. 201–209. doi: 10.1007/978-3-031-88714-7_18.
[8] H. Zhang et al., "MMLN: Leveraging Domain Knowledge for Multimodal Diagnosis," in Bioinformatics Research and Applications, vol. 13559, Cham: Springer Nature Switzerland, 2022, pp. 204-216. doi: 10.1007/978-3-031-23198-8_18.
[9] X. Hou et al., "Radiographic Reports Generation via Retrieval Enhanced Cross-modal Fusion," in Proc. 2024 IEEE Int. Conf. Bioinformatics Biomed. (BIBM), Dec. 2024, pp. 2032-2039. doi: 10.1109/BIBM62325.2024.10821990. provide me pdf or doc for this all reference papers along with their links
What Will Be Provided:
Main Project Details: Documentation/summary of the "Causal Multimodal Diagnostic Agent" project.
Reference Papers: A list of IEEE-standard papers related to the topic.
Scope of Work: You are required to:
Review: Read the provided project details and the additional reference papers.
Synthesize: Combine the observations, methods, and findings from all provided sources.
Draft: Write a structured research paper (Abstract, Introduction, Literature Review, Methodology, Conclusion) that serves as the "base paper" for this project.
Format: The paper must follow standard research paper formatting (IEEE style preferred).
Strict Constraints:
Budget: ₹500 INR (Fixed). This is for a base paper draft. Please do not bid if you cannot work within this budget.
Deadline: December 26th. This is a strict deadline. Late submissions will not be accepted.
Payment Terms & Revision Policy:
Payment Condition: Payment will only be released after the final draft is approved.
Revisions: You must be available to make specific updates or corrections based on my feedback.
How to Apply: Please confirm you have read the budget and deadline constraints and are comfortable with the payment terms (release upon successful updates).
below are the paper reference to be strictly :
[1] Z. Yao et al., "Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis," in Proc. 2024 IEEE 2nd Int. Conf. Sens., Electron. Comput. Eng. (ICSECE), May 2024, pp. 1-6. doi: 10.1109/ICSECE61636.2024.10729527.
[2] N. Aksoy, S. Sharoff, S. Baser, N. Ravikumar, and A. F. Frangi, "Beyond images: an integrative multi-modal approach to chest xray report generation," Front. Radiol., vol. 4, p. 1339612, Feb. 2024. doi: 10.3389/fradi.2024.1339612.
[3] C. Pellegrini, M. Keicher, E. Özsoy, P. Jiraskova, R. Braren, and N. Navab, "Xplainer: From X-Ray Observations to Explainable Zero-Shot Diagnosis," in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, vol. 14224, Cham: Springer Nature Switzerland, 2023, pp. 420–429. doi: 10.1007/978-3-031-43904-9_41.
[4] A. Lucieri et al., "ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions," Comput. Methods Programs Biomed., vol. 215, p. 106620, Mar. 2022. doi: 10.1016/j.cmpb.2022.106620.
[5] J. Sun, L. Wang, and Y. Zheng, "Lesion Guided Explainable Few Weak-Shot Medical Report Generation," in Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, vol. 13435, Cham: Springer Nature Switzerland, 2022, pp. 615–625. doi: 10.1007/978-3-031-16443-9_59.
[6] Q. Han, J. Liu, Z. Qin, and Z. Zheng, "Integrating MedCLIP and Cross-Modal Fusion for Automatic Radiology Report Generation," in Proc. 2024 IEEE Int. Conf. Big Data (BigData), Dec. 2024, pp. 7313-7317. doi: 10.1109/bigdata62323.2024.10825240.
[7] H. Alam, D. Srivastav, and M. A. Kadir, "Towards Interpretable Radiology Report Generation via Concept Bottlenecks using a Multi-Agentic RAG," in Advances in Information Retrieval, vol. 15574, Cham: Springer Nature Switzerland, 2025, pp. 201–209. doi: 10.1007/978-3-031-88714-7_18.
[8] H. Zhang et al., "MMLN: Leveraging Domain Knowledge for Multimodal Diagnosis," in Bioinformatics Research and Applications, vol. 13559, Cham: Springer Nature Switzerland, 2022, pp. 204-216. doi: 10.1007/978-3-031-23198-8_18.
[9] X. Hou et al., "Radiographic Reports Generation via Retrieval Enhanced Cross-modal Fusion," in Proc. 2024 IEEE Int. Conf. Bioinformatics Biomed. (BIBM), Dec. 2024, pp. 2032-2039. doi: 10.1109/BIBM62325.2024.10821990. provide me pdf or doc for this all reference papers along with their links