docs: document MedGemma CT setup on 3060

2026-11-06 10:47:21 +03:00
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+# MedGemma and CT processing on server 3060
+
+Date: 2026-11-06
+
+## Server access
+
+Server `3060` is reachable from the local Moscow network at `192.168.1.49`.
+
+SSH access:
+
+```bash
+ssh -i ~/.ssh/id_ed25519-3060 naeel@192.168.1.49
+```
+
+Windows key path used for the local setup:
+
+```text
+Y:\MY\Nubes\id_ed25519
+```
+
+The private key itself is not stored in this repository or in this document.
+
+## Host resources
+
+Verified on the host:
+
+- OS: Ubuntu 24.04.5 LTS, x86_64
+- CPU: AMD Ryzen 5 5600, 6 cores / 12 threads
+- RAM: 15 GiB
+- Swap: 4 GiB
+- GPU: NVIDIA GeForce RTX 3060, 12 GiB VRAM
+- NVIDIA driver: 580.178.04
+- Disk: NVMe, 476.9 GB; approximately 141 GB free at verification time
+
+The host is suitable for local inference with MedGemma 4B. The 27B variants are not a practical fit for this hardware.
+
+## CT data
+
+The source CT directory was copied to the host:
+
+```text
+/home/naeel/MED_DATA/CY_CT
+```
+
+Verification after transfer:
+
+- 1,669 files
+- approximately 1.3 GB
+- source data was not modified
+
+No DICOM headers, patient identifiers, or image content are stored in this repository.
+
+## Installed environment
+
+The isolated Python environment is:
+
+```text
+/home/naeel/MED_DATA/medgemma-venv
+```
+
+Activate it with:
+
+```bash
+source /home/naeel/MED_DATA/medgemma-venv/bin/activate
+```
+
+Installed and verified components include:
+
+- PyTorch `2.11.0+cu128`
+- Transformers `5.22.1`
+- torchvision
+- accelerate
+- bitsandbytes
+- huggingface_hub
+- pydicom
+- SimpleITK
+- NumPy
+- Pillow
+- safetensors
+
+CUDA verification succeeded and PyTorch detected the RTX 3060 with 11.60 GiB usable VRAM.
+
+## MedGemma model
+
+Model:
+
+```text
+google/medgemma-4b-it
+```
+
+Local path:
+
+```text
+/home/naeel/MED_DATA/models/medgemma-4b-it
+```
+
+Model download verification:
+
+- 15 of 15 files downloaded
+- approximately 8.64 GB
+- `AutoProcessor` loaded successfully
+- `AutoModelForImageTextToText` loaded successfully with BF16 and `device_map="auto"`
+- model weights loaded on the RTX 3060
+
+Hugging Face access is gated. The token is stored in the user's Hugging Face cache on the server and is not recorded here. To authenticate a new session, use:
+
+```bash
+/home/naeel/MED_DATA/medgemma-venv/bin/hf auth login
+```
+
+Do not put the token in this repository, shell history, or chat messages.
+
+## Smoke test
+
+A real read-only smoke test was run against one readable DICOM slice from `CY_CT`.
+
+The test performed the following steps:
+
+1. Read one DICOM image with `pydicom`.
+2. Applied `RescaleSlope` and `RescaleIntercept` when present.
+3. Applied a soft-tissue CT window: center 40, width 400.
+4. Converted the slice to an 8-bit PIL image.
+5. Sent one image and a short prompt to MedGemma.
+
+Result:
+
+- DICOM found: yes
+- CUDA inference: successful
+- elapsed time: approximately 3.5 seconds
+- response: a generic description of bones, soft tissue, and air; no diagnosis was generated
+
+This was only a technical inference test. It was not a full-series CT analysis.
+
+## Correct CT processing pipeline
+
+MedGemma 4B is an image-text model. It is not a ready-made 3D CT analysis system. The model card describes evaluation primarily on single-image tasks and does not establish reliable multi-image or full-volume CT interpretation.
+
+A proper pipeline should be:
+
+```text
+DICOM series
+  -> group by SeriesInstanceUID
+  -> sort by ImagePositionPatient
+  -> convert pixels to HU
+  -> resample the 3D volume
+  -> apply appropriate CT windows
+  -> run a task-specific 3D CT model
+  -> send selected images and structured results to MedGemma
+  -> save structured output and provenance
+```
+
+Useful ready-made components:
+
+- 3D Slicer for DICOM viewing and volume preparation
+- TotalSegmentator for anatomical CT segmentation
+- MONAI Label for interactive model-based segmentation
+- nnU-Net for a specific segmentation task with a compatible model
+- OHIF Viewer for browser-based DICOM viewing
+
+Running all 1,669 files independently through MedGemma would produce disconnected slice descriptions. At the measured rate it would take roughly 1 hour 40 minutes of inference, plus DICOM and output overhead. That is not equivalent to a 3D CT analysis.
+
+## Current state
+
+Completed:
+
+- local SSH access to server 3060 verified
+- `MED_DATA/CY_CT` copied to the server
+- isolated GPU Python environment installed
+- MedGemma 4B downloaded and loaded successfully
+- one-slice DICOM inference verified
+
+Not yet implemented:
+
+- DICOM series grouping and validation
+- 3D volume assembly and resampling
+- task-specific CT segmentation or detection model
+- persistent API or web service
+- full-series processing and result aggregation
+
+## Safety and data handling
+
+The model output is preliminary and may be incorrect. The MedGemma model card requires downstream validation and meaningful adaptation for a specific use case. This repository records installation and engineering checks only; it does not contain a clinical conclusion or patient data.