Patrick S.

Key Skills

Software

Don't disclose

Top Subject Matter

Medical image segmentation (skin lesion)

Industrial defect detection (object detection)

3D point-cloud semantic segmentation

Top Data Types

Image

3D Sensor

Top Task Types

Segmentation

Object Detection

Tracking

Freelancer Overview

Skin Lesion Image Segmentation project (CA-GAT) on ISIC2018. Core strengths include Don't disclose. Education includes Master of Science, University of Malaya and Bachelor of Science, Guilin University of Electronic Technology. AI-training focus includes data types such as Medical, DICOM, and Image and labeling workflows including Segmentation, Object Detection, and Tracking.

Labeling Experience

3D Object Detection and Tracking Viewer (KITTI/Waymo-style)

Don't disclose3D SensorTracking

Reproduced and optimized a 3D object detection and tracking viewer for LiDAR-camera calibration and multi-frame tracking visualization on KITTI/Waymo-style data. Reviewed detection and tracking performance using established tracking and detection metrics to support output evaluation and quality checks. Assisted in ensuring interpretation of 3D bounding boxes and tracking results in a structured way for acceptance-style review. • Implemented/optimized LiDAR point cloud and calibration visualization workflows • Supported 3D bounding box and multi-frame tracking visualization • Evaluated results using HOTA, MOTA, and related detection/tracking metrics • Reviewed ID-related and detection precision/recall indicators for quality assessment

Not specified

Urban-Scale Point Cloud Semantic Segmentation project (Transformer) on SensatUrban / Semantic3D

Don't disclose3D SensorSegmentation

Worked on urban-scale 3D point-cloud semantic segmentation using transformer-based architectures on SensatUrban and Semantic3D. Performed dataset evaluation with semantic-category IoU review to check label consistency and segmentation quality across large scenes. Applied point sampling and attentive pooling to preserve point features while reducing computational cost. • Adapted Point Transformer style model components for large-scale point clouds • Conducted semantic IoU metric review for SensatUrban and Semantic3D • Focused on preserving feature quality during transition-down processing • Evaluated segmentation outputs across different urban remote sensing datasets

Not specified

Strip Steel Surface Defect Detection project (MSC-DNet) on NEU-DET / GC10-DET

Don't discloseImageObject Detection

Supported an industrial surface defect detection workflow on NEU-DET/GC10-DET, centered on localizing and classifying defects under low contrast and scale variation. Conducted dataset preprocessing, baseline reproduction, ablation-style comparisons, and organized experimental results to enable reliable detection QA. Reviewed detection outcomes using mean Average Precision and other detection performance measures to verify model behavior for defect categories. • Prepared dataset inputs and baseline training/evaluation runs for NEU-DET/GC10-DET • Conducted ablation analysis around multi-scale context modules (PADC, FESM, AIS) • Organized and reviewed detection experiments for defect category consistency • Reported and interpreted mAP results for NEU-DET and GC10-DET

Not specified

Skin Lesion Image Segmentation project (CA-GAT) on ISIC2018

Don't discloseSegmentation

Contributed to a medical image segmentation project on ISIC2018, addressing challenging lesion boundaries and occlusions via structured experimental preprocessing and evaluation. Focused on establishing and reviewing quantitative segmentation metrics to support consistent dataset handling and model output assessment. Applied attention-based deep learning components and a gated axial transformer backbone to improve contextual understanding for accurate mask-based predictions. • Used U-Net style encoder-decoder design with gated axial transformer layers • Performed preprocessing steps including resize, normalization, and augmentation • Reviewed experimental results with Dice and IoU metric tracking • Evaluated performance specifically on ISIC2018 segmentation outcomes

Not specified

Education

Work History