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Novel Architecture of RPA In Oral Cancer Lesion Detection

Conference: CVPR2025
arXiv: 2603.10928
Code: To be confirmed
Area: Medical Imaging
Keywords: Oral cancer detection, RPA, EfficientNetV2B1, Design patterns, Batch processing

TL;DR

This paper integrates Singleton and Batch Processing design patterns into a Python-based RPA automation pipeline, combining them with the EfficientNetV2B1 model for oral cancer lesion detection, achieving a 60-100× inference speedup compared to traditional RPA platforms such as UiPath and Automation Anywhere.

Background & Motivation

  • Early and accurate detection of oral cancer is critical for improving patient survival rates, but clinical workflows are still plagued by subjective human judgment, delays, and inconsistent decision-making.
  • Robotic Process Automation (RPA) has been utilized in healthcare to automate tasks such as image processing, laboratory data management, and patient data analysis.
  • Although existing RPA platforms (e.g., UiPath, Automation Anywhere) are user-friendly, they are highly inefficient for computationally intensive tasks: approximately 78% of processing time is spent on overhead (model reloading, activity transitions, data serialization), while only 22% is allocated to actual inference.
  • There is a need for a hybrid solution that combines the workflow orchestration benefits of RPA with the high-performance computing capabilities of Python.

Method

Dataset and Preprocessing

  • A dataset of approximately 3000 clinical oral images is classified into 4 major categories (Healthy, Benign, OPMD, Oral Cancer) and 16 subcategories.
  • Data split: 70% training, 15% validation, and 15% testing (stratified sampling).
  • Preprocessing: Pixel normalization to \([0, 1]\) and ImageNet mean/std standardization.
  • Data augmentation: Using the Albumentations library, 5 transformations are applied per training sample (flipping, rotation, brightness/contrast adjustment, random cropping), and random duplication is performed for classes with fewer than 200 samples.

Model Architecture

  • ImageNet pre-trained EfficientNetV2B1 is adopted as the feature extractor.
  • The input size is \(224 \times 224 \times 3\), with a fully connected Dense + Softmax layer appended at the top.
  • Two-stage training:
  • Feature extraction phase: Freezing base layers, 15 epochs, learning rate \(1\text{e-}3\).
  • Fine-tuning phase: Partially unfreezing deep layers, 10 epochs, learning rate \(1\text{e-}5\).
  • Adam optimizer + categorical cross-entropy, with a batch size of 32.
  • Early stopping + ReduceLROnPlateau + checkpointing based on the best validation accuracy.

RPA Implementation Comparison

  1. OC-RPAv1: Python-based sequential RPA-style processing, loading the model to predict one image at a time.
  2. OC-RPAv2: Integrates Singleton and Batch Processing design patterns.
    • Singleton: The model is loaded only once and retained in memory, avoiding repeated reloading overhead.
    • Batch Processing: Batches images to leverage GPU parallel inference.
    • UiPath manages the automation pipeline and invokes Python functions to execute inference.

Workflow Synchronization and Security

  • Image batches are processed sequentially, and the next batch is only processed after each file is classified and logged, avoiding data collisions.
  • Try-Catch exception handling ensures workflow continuity.
  • Processing is conducted on local secure workstations with anonymized file paths and restricted access controls.
  • Processed files are moved to a separate directory to ensure data integrity.
  • Based on the CLASEG framework by Al-Ali et al., which integrates multi-class classification and segmentation for differential diagnosis of oral lesions.
  • Follows the LMV-RPA approach of Abdellaif et al., supplementing standard RPA with enhanced Python automation.
  • Kim et al. previously demonstrated the speedup effects of a hybrid RPA+Python architecture in computer-aided cancer detection on pathological images.
  • This paper further introduces design patterns (Singleton + Batch) into this hybrid architecture and quantifies the acceleration ratio.

Key Experimental Results

Inference Speed Comparison (31 Test Images)

Platform Total Time Average Time per Image
UiPath 80 s 2.58 s
Automation Anywhere 75 s 2.42 s
OC-RPAv1 (Python) 8.65 s 0.28 s
OC-RPAv2 (Python+DP) 1.96 s 0.06 s
  • OC-RPAv2 is ~43× faster than UiPath and ~40× faster than Automation Anywhere.
  • OC-RPAv2 is ~4.4× faster than OC-RPAv1.
  • The introduction of design patterns compresses the execution time of the Python pipeline from 8.65 s to 1.96 s.

Scalability Estimation

  • For 2500 images: UiPath requires 1.8 hours, while OC-RPAv2 takes less than 3 minutes.

Highlights & Insights

  1. High Engineering Practicality: The combination of Singleton and Batch Processing design patterns is simple yet effective, lowering the barrier to deployment.
  2. Substantial Acceleration: Achieving 60-100× acceleration compared to standard RPA platforms, showing clear value for clinical deployment.
  3. Cost Reduction: Reducing hardware idle time and RPA licensing costs, with the paper claiming a 40× cost reduction.
  4. Hybrid Architecture Approach: Leverages the strengths of both worlds, with RPA handling workflow orchestration and Python taking charge of computationally intensive inference.
  5. 16-Class Oral Lesion Classification: Covers 4 major categories (Healthy, Benign, OPMD, Oral Cancer) and several subcategories, providing fine-grained classification.

Limitations & Future Work

  1. Extremely Small Test Set: Only 31 test images are used, indicating very low statistical reliability, which makes it impossible to draw robust speed benchmarking conclusions.
  2. Lack of Classification Accuracy Metrics: The paper focuses disproportionately on speed comparison, failing to report critical classification metrics such as accuracy, precision, and recall on the test set.
  3. Limited Technical Contribution: Singleton and Batch Processing are fundamental software engineering design patterns; the core "innovation" is closer to engineering optimization rather than novel academic contributions.
  4. Unfair Comparison: The overhead of RPA platforms mainly stems from GUI automation and activity transitions; hence, comparing them directly to raw Python pipelines is inherently a comparison of different paradigms.
  5. Poor Writing Quality: Contains redundant paragraphs, non-standard formatting, and incomplete references.
  6. Absence of Clinical Validation: The system has not been deployed or validated in a real-world clinical setting, leaving scalability claims unsupported.
  7. Significant Gap with CVPR Standard: The overall work resembles an engineering report rather than a top-tier conference paper, lacking academic depth.
  8. No Comparison with State-of-the-Art Methods: Lacks comparative analysis of detection accuracy against mainstream CNN or ViT-based oral cancer detection methods.
  9. Insufficient Dataset Details: Fails to report key dataset characteristics such as sample distribution among subcategories and precise sample counts post-augmentation.
  10. No Ablation Study: Fails to isolate and validate the individual contributions of Singleton and Batch Processing.

Rating

  • Novelty: ⭐⭐ — Singleton and Batch Processing are foundational design patterns, lacking methodological innovation.
  • Experimental Thoroughness: ⭐ — Only 31 test images and no classification performance metrics, showing highly insufficient experimental design.
  • Writing Quality: ⭐⭐ — Redundant and repetitive text, chaotic formatting, with multiple duplicated paragraphs.
  • Value: ⭐⭐ — While the engineering workflow has practical reference value, the academic contribution is insufficient for top-tier publication.
  • Overall: ⭐⭐ — More suitable as an engineering technical report than as an academic paper reference.