Refine BTech/MTech Diagnostic Research Paper
Budget: ₹1,500 – ₹12,500 INR
PROJECT: Refurnish my BTech/MTech research paper to match the exact structure, tone, and detail level of the reference PDF I provide.
Topic: Causal Multimodal Diagnostic Agent Combining Chest X-ray Images + Clinical Reports for Thoracic Diseases
WHAT I PROVIDE:
1. Reference Paper PDF: You MUST replicate its section headings, table formats, equation style, and writing tone 100%.
2. My Code: model.py, train.py, config.yaml - ResNet-50 + ClinicalBERT + cross-modal attention + label GNN
3. My Results: results.csv with AUROC/F1 per class, loss_curves.png, gradcam_effusion.png, gradcam_pneumothorax.png
4. Dataset Info: MIMIC-CXR subset, 1,495 PA-view image-report pairs, 14 CheXpert labels, patient-level 70/10/20 split. NO full 500GB dataset needed.
5. Target: BTech/MTech final submission + college conference like ICICCT/INDICON
YOUR SCOPE - RENEW ONLY THESE 4 SECTIONS. KEEP ALL OTHER HEADINGS IDENTICAL:
1. SECTION I. INTRODUCTION [Complete rewrite, ~400 words, 4 paragraphs]
Para 1: Thoracic disease burden + diagnostic challenges
Para 2: CXR as primary tool + radiologist workload/error rates
Para 3: Deep learning CNN success + black-box problem in healthcare
Para 4: XAI + summary of our proposed multimodal work with 3 contributions
2. SECTION III. METHODOLOGY [Edit ONLY these subheadings]
4.1 System Architecture: Pipeline text + describe Fig 1: Data → Preprocessing → ResNet-50 + ClinicalBERT → Fusion → Classifier → Grad-CAM
4.2 Dataset Description: Create Table 1 like reference - Source: MIMIC-CXR, Pairs: 1,495, Classes: 14, Split: patient-level 70/10/20. Add sentence: "Due to computational constraints, we used a curated subset following standard practice [ref]."
4.3 Data Preprocessing: Bullets - Resizing 224×224, Normalization ImageNet, Augmentation, Balancing
4.4 Mathematical model: Add equations for Softmax + Weighted BCE Loss for multi-label. Remove "causal" if my code has no do-calculus. Rename to "Dynamic Modality Gating".
Keep 4.5-4.11 same, fix grammar only.
3. SECTION IV. RESULTS & DISCUSSIONS [Rewrite 4.5, 4.7, 4.8 completely]
4.5 Multimodal Fusion Benefit: Create Table 2: Model | Macro-AUROC | Micro-AUROC | F1. Rows: Image-only, Text-only, Ours. Use my CSV data.
4.7 Comparison to Related Work: Compare to 3 papers 2020-2024. Add table. No fake SOTA claims.
4.8 Limitations and Threats to Validity: Write 1 paragraph: "subset size 1,495 pairs may limit generalization", "single-site MIMIC data", "no radiologist reader study", "causal claims require external validation".
Keep 4.4, 4.6 same, grammar fix only.
4. SECTION V. CONCLUSION [Complete rewrite, ~200 words]
Structure: What we built + macro-AUROC 0.9356 + limitation + future work: "external validation on CheXpert". Remove clinical deployment claims.
NON-NEGOTIABLE REQUIREMENTS:
1. Turnitin Plagiarism <10% - screenshot required
2. AI Detection <15% - GPTZero/Originality.ai screenshot required
3. Keep ALL section numbers I, II, III, IV, V and subheadings 4.1-4.8 identical to list above
4. All technical details must match my provided code. No fabricated equations or results
5. Sign NDA - code/topic confidential, no reuse
6. Track Changes ON in.docx
7. IEEE citation style, 15+ refs 2020-2025
DELIVERABLES:
1. Edited.docx with Track Changes
2. Clean final.docx +.pdf
3. Turnitin report <10%
4. AI report <15%
5. References.bib or list
BUDGET: INR 2500-4000fixed / $30-$55 USD. Timeline: 2 weeks.
Milestones: 50% after Draft of I + III approved, 50% after Final + reports.
TO APPLY - ANSWER ALL 3 OR BID REJECTED:
1. Confirm: "I will keep all headings I-V and subheadings 4.1-4.8 identical. I will add the limitation sentence about 1,495 pairs in 4.8."
2. Share link/screenshot of 1 medical AI paper you edited + its Turnitin score.
3. My code has no do-calculus. What will you rename 'Modality Causal Block' to in Section 4.4?
AUTO-REJECT: Generic bids, no medical sample, anyone who adds fake causal math, or expects me to provide 500GB data.
Topic: Causal Multimodal Diagnostic Agent Combining Chest X-ray Images + Clinical Reports for Thoracic Diseases
WHAT I PROVIDE:
1. Reference Paper PDF: You MUST replicate its section headings, table formats, equation style, and writing tone 100%.
2. My Code: model.py, train.py, config.yaml - ResNet-50 + ClinicalBERT + cross-modal attention + label GNN
3. My Results: results.csv with AUROC/F1 per class, loss_curves.png, gradcam_effusion.png, gradcam_pneumothorax.png
4. Dataset Info: MIMIC-CXR subset, 1,495 PA-view image-report pairs, 14 CheXpert labels, patient-level 70/10/20 split. NO full 500GB dataset needed.
5. Target: BTech/MTech final submission + college conference like ICICCT/INDICON
YOUR SCOPE - RENEW ONLY THESE 4 SECTIONS. KEEP ALL OTHER HEADINGS IDENTICAL:
1. SECTION I. INTRODUCTION [Complete rewrite, ~400 words, 4 paragraphs]
Para 1: Thoracic disease burden + diagnostic challenges
Para 2: CXR as primary tool + radiologist workload/error rates
Para 3: Deep learning CNN success + black-box problem in healthcare
Para 4: XAI + summary of our proposed multimodal work with 3 contributions
2. SECTION III. METHODOLOGY [Edit ONLY these subheadings]
4.1 System Architecture: Pipeline text + describe Fig 1: Data → Preprocessing → ResNet-50 + ClinicalBERT → Fusion → Classifier → Grad-CAM
4.2 Dataset Description: Create Table 1 like reference - Source: MIMIC-CXR, Pairs: 1,495, Classes: 14, Split: patient-level 70/10/20. Add sentence: "Due to computational constraints, we used a curated subset following standard practice [ref]."
4.3 Data Preprocessing: Bullets - Resizing 224×224, Normalization ImageNet, Augmentation, Balancing
4.4 Mathematical model: Add equations for Softmax + Weighted BCE Loss for multi-label. Remove "causal" if my code has no do-calculus. Rename to "Dynamic Modality Gating".
Keep 4.5-4.11 same, fix grammar only.
3. SECTION IV. RESULTS & DISCUSSIONS [Rewrite 4.5, 4.7, 4.8 completely]
4.5 Multimodal Fusion Benefit: Create Table 2: Model | Macro-AUROC | Micro-AUROC | F1. Rows: Image-only, Text-only, Ours. Use my CSV data.
4.7 Comparison to Related Work: Compare to 3 papers 2020-2024. Add table. No fake SOTA claims.
4.8 Limitations and Threats to Validity: Write 1 paragraph: "subset size 1,495 pairs may limit generalization", "single-site MIMIC data", "no radiologist reader study", "causal claims require external validation".
Keep 4.4, 4.6 same, grammar fix only.
4. SECTION V. CONCLUSION [Complete rewrite, ~200 words]
Structure: What we built + macro-AUROC 0.9356 + limitation + future work: "external validation on CheXpert". Remove clinical deployment claims.
NON-NEGOTIABLE REQUIREMENTS:
1. Turnitin Plagiarism <10% - screenshot required
2. AI Detection <15% - GPTZero/Originality.ai screenshot required
3. Keep ALL section numbers I, II, III, IV, V and subheadings 4.1-4.8 identical to list above
4. All technical details must match my provided code. No fabricated equations or results
5. Sign NDA - code/topic confidential, no reuse
6. Track Changes ON in.docx
7. IEEE citation style, 15+ refs 2020-2025
DELIVERABLES:
1. Edited.docx with Track Changes
2. Clean final.docx +.pdf
3. Turnitin report <10%
4. AI report <15%
5. References.bib or list
BUDGET: INR 2500-4000fixed / $30-$55 USD. Timeline: 2 weeks.
Milestones: 50% after Draft of I + III approved, 50% after Final + reports.
TO APPLY - ANSWER ALL 3 OR BID REJECTED:
1. Confirm: "I will keep all headings I-V and subheadings 4.1-4.8 identical. I will add the limitation sentence about 1,495 pairs in 4.8."
2. Share link/screenshot of 1 medical AI paper you edited + its Turnitin score.
3. My code has no do-calculus. What will you rename 'Modality Causal Block' to in Section 4.4?
AUTO-REJECT: Generic bids, no medical sample, anyone who adds fake causal math, or expects me to provide 500GB data.