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v2.1 - UI Redesign (carries v2.0 model assets)

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@Cureeeeeeee Cureeeeeeee released this 25 May 11:04
v2.1
ec8a763

DermaSense v2.1 — UI Redesign

Builds on v2.0 ("Phase C v2 production deploy + Phase E external
validation") with a complete Flutter UI redesign across all five screens.

What's new in v2.1

Two-stage UI redesign (Stage 1 + Stage 2)

  • DermaSense design system in mobile_app/lib/theme/design_tokens.dart
    (palette, typography ramp, spacing, hairline border, no shadow)
  • HomeScreen redesigned (Stage 1, commit 749ec64)
  • Classification, Result, Model Performance, Safety / About redesigned
    (Stage 2, commit d39a3b9)

Result screen

  • Risk-state-coloured hero (urgent / amber / benign)
  • 200 ms cross-fade Grad-CAM attention toggle (single model)
  • 2×2 per-model ensemble grid with shared lightbox modal
  • Top-3 differential with class chips and a Calibration explainer card

Model Performance screen

  • Test-set evaluation header (ResNet50 v2 default)
  • Per-class recall with ResNet50 v2 column tinted; <70% warning glyphs
  • 7×7 confusion-matrix heatmap on ResNet50 v2 HAM10000 test split
  • Known Limitations card (5 items including ISIC 2019 external-validation
    drop: v2 single-model mel recall 73.40% → 37.09%)

Safety / About screen

  • System Identity, Intended Use, "Not Intended For"
  • Datasets card (HAM10000 + ISIC 2019, both CC BY-NC 4.0)
  • Calibration temperatures table with v2 row DEPLOYED chip
  • License & Attribution card (code MIT, model weights CC BY-NC 4.0)

What's unchanged from v2.0

  • All backend code (FastAPI, 5 endpoints)
  • All model checkpoints — bit-identical to v2.0
  • All calibration files — bit-identical to v2.0
  • Phase A (calibration), Phase B (Grad-CAM), Phase C (v2 training),
    Phase E (external validation) results

Assets

The 12 v2.0 release assets are carried over verbatim to v2.1 so this
release is self-contained (clone + download = working system).

Total: 12 files, ~317 MB

  • 4 base CNN checkpoints + their calibration JSON (ResNet50 v1, DenseNet121,
    EfficientNet-B0, MobileNetV3 Small)
  • 1 ResNet50 v2 checkpoint + calibration (single-model default,
    focal loss + balanced sampler)
  • 1 ResNet50 v1 backup checkpoint + calibration (preserved for ensemble
    stability)

How to reproduce

git clone https://github.com/Cureeeeeeee/CNN-Based-Skin-Lesion-Classification-for-Early-Skin-Cancer-Detection.git
cd CNN-Based-Skin-Lesion-Classification-for-Early-Skin-Cancer-Detection
git checkout v2.1

# Download all 12 assets from this release into runs/
gh release download v2.1 --dir runs/

# Backend
pip install -r requirements.txt
uvicorn src.skinlesion.api:app --host 0.0.0.0 --port 8126

# Flutter web
cd mobile_app
flutter run -d chrome

Co-author

Co-Authored-By: Claude noreply@anthropic.com

Assets 14
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