Domain Analysis: Computer Vision & Medical Imaging
Generated July 7, 2026 from 200 real arXiv papers (4 queries x 50 results)
Source Queries
vision-dcgan-medicalvision-multimodal-indovision-synthetic-detectvision-yolo-transfer
Total papers scanned: 200
Category Distribution
| Category | Count |
|---|---|
| cs.CV | 145 |
| cs.LG | 67 |
| cs.AI | 59 |
| eess.IV | 55 |
| cs.CL | 43 |
| stat.ML | 12 |
| cs.HC | 9 |
| cs.CR | 7 |
| cs.CY | 7 |
| cs.MM | 5 |
| cs.NE | 4 |
| cs.RO | 4 |
| cs.IR | 3 |
| physics.med-ph | 2 |
| eess.SP | 2 |
| cs.IT | 2 |
| cs.SD | 1 |
| physics.pop-ph | 1 |
| astro-ph.IM | 1 |
| cs.DB | 1 |
| cs.OH | 1 |
| cs.GR | 1 |
| physics.ed-ph | 1 |
| cs.SE | 1 |
| cs.DC | 1 |
| astro-ph.CO | 1 |
| astro-ph.GA | 1 |
| physics.ao-ph | 1 |
| stat.ME | 1 |
| cs.NI | 1 |
| cs.SI | 1 |
| cs.ET | 1 |
| quant-ph | 1 |
Key Findings & Gaps
Finding 1: YOLO Edge Deployment is Active but Context-Specific
MS-YOLO (arXiv:2509.21696) demonstrates MobileNetV4 + SlideLoss for infrared detection at 6.7 GFLOPs. DAMO-YOLO (arXiv:2211.15444) uses NAS for efficient backbone search. However, deployment in developing-country contexts (Indonesian traffic, agriculture, surveillance) is unexplored.
Gap: YOLO edge deployment for Indonesian-specific use cases is absent from literature.
Finding 2: Medical Image Generation Needs Better Evaluation Metrics
The Frechet Radiomic Distance (FRD) paper (arXiv:2412.01496) convincingly shows that FID is insufficient for medical images. However, FRD has not been evaluated on low-resource clinical settings or on modalities beyond CT and mammography.
Gap: FRD evaluation in low-resource clinical settings is missing.
Finding 3: Synthetic AI Image Detection Lacks Generalization Studies
Papers on synthetic AI image detection identify domain shift between GAN-generated and diffusion-generated images. Cross-generator generalization (detecting images from unseen generators) remains poor, and no study addresses this for Indonesian-context images.
Gap: Domain-generalizable synthetic image detection for real-world deployment.
Finding 4: Indonesian Vision-Language Models are Absent
Despite the multilingual multimodal tutorial (arXiv:2605.17152) covering Maya and PALO models, no study has produced an Indonesian-specific VLM. The closest models (mPLUG, PALO) support Indonesian as one of many languages but lack Indonesian cultural visual understanding.
Gap: Zero Indonesian-specific vision-language model exists.
Finding 5: DCGAN for Medical Imaging Has Limited Clinical Validation
TransMorph (arXiv:2111.10480) and FRD (arXiv:2412.01496) advance medical image analysis, but GAN-generated medical images lack clinical validation studies with domain experts. The utility for low-resource settings where training data is scarce is particularly unexplored.
Gap: No clinical validation framework for GAN-generated medical images exists.
Recommended Thesis Directions
- IndoVLM: A Vision-Language Model for Indonesian Visual Understanding
- Synthetic-to-Real Domain Adaptation for AI-Generated Image Detection
- DCGAN-Augmented Medical Image Classification for Low-Resource Clinical Settings
- YOLO-Based Object Detection for Indonesian Road Infrastructure Monitoring
- Frechet Radiomic Distance in Low-Resource Settings: Validation on Portable Imaging
- Federated Object Detection for Privacy-Preserving Surveillance
- Multimodal Indonesian Cultural Heritage Recognition
Key Papers Referenced
- “Fréchet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets” –
2412.01496v2 - “TransMorph: Transformer for unsupervised medical image registration” –
2111.10480v6 - “Cross-dimensional transfer learning in medical image segmentation with deep learning” –
2307.15872v1 - “Building medical image classifiers with very limited data using segmentation networks” –
1808.05205v1 - “GAN-GA: A Generative Model based on Genetic Algorithm for Medical Image Generation” –
2401.00314v1 - “Test-time generative augmentation for medical image segmentation” –
2406.17608v2 - “Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation” –
2004.04668v4 - “Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation” –
2006.16806v1 - “A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysis” –
1910.02923v2 - “MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentation” –
2408.08070v2 - “Distribution Matching Losses Can Hallucinate Features in Medical Image Translation” –
1805.08841v3 - “Privacy Preserving Image Registration” –
2205.10120v7 - “Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imaging” –
2204.03547v1 - “MedP-CLIP: Medical CLIP with Region-Aware Prompt Integration” –
2604.11197v3 - “Multi-Objective Dual Simplex-Mesh Based Deformable Image Registration for 3D Medical Images – Proof of Concept” –
2202.11001v1
Note: S2 API was rate-limited (HTTP 429).