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Thales Matheus M. Santos

Physician · Radiologist · Computer Science Student

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About Me

I'm a physician and computer science undergraduate exploring how software engineering and artificial intelligence can enhance radiological practice. My work is focused on clinical radiology, artificial intelligence and modern software engineering to create practical, scalable solutions:

  • DICOM visualization and PACS integration
  • Generative AI (LLMs, local inference, RAGs/finetuning)
  • Lightweight infrastructure for healthcare systems (DICOM viewers, NAS, containers, Proxmox)
  • Full-stack development tailored to clinical workflows

Tech Stack

  • Languages: Python · Swift · C++ · Java · JavaScript · Dart
  • Frameworks: Django · SwiftUI · Metal/SceneKit · Flutter
  • Infra / DevOps: Docker · Proxmox · QNAP (& other open source NAS) · Tailscale · Git
  • AI/ML: PyTorch · Transformers · scikit-learn · Ollama
  • Medical Imaging: DcmSwift · DCMTK · GDCM · OsiriX/Horos · 3D Slicer · Orthanc

Current Projects

// Package.swift
dependencies: [
    .package(url: "https://github.com/ThalesMMS/DcmSwift.git", branch: "main")
]
// Package.swift
dependencies: [
    .package(url: "https://github.com/ThalesMMS/Swift-DICOM-Decoder.git", branch: "main")
]
  • Isis DICOM Viewer (iOS) – mobile DICOM viewer with 2D/MPR/Volumetric rendering and PACS integration.
  • JFlutter — modern Flutter port of the classic JFLAP tool for automata & formal language theory. Mobile‑first, touch‑optimised UI for creating and simulating automata/CFGs.
  • TotalSegmentator Horos Plugin - bringing the modern Total Segmentator to the open source Horos Project!
  • WALL-ET - Mobile Bitcoin wallet
  • Orthanc for QNAP – Custom .qpkg packaging
  • LLMs + Radiology – Fine-tuning and RAG pipelines with anonymized radiology data

Dev Tools & Setup

  • macOS + Windows (development/testing)
  • NAS: QNAP QTS with Dockerized services
  • Preferred stack: Django + Python APIs, Swift/Flutter
  • Experimenting with LM Studio, Ollama, ComfyUI

Interests

  • DICOM Viewers
  • DICOM servers in low-resource environments
  • AI-assisted radiological decision-making
  • Local-first imaging platforms

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