README.md

---
license: cc-by-4.0
tags:
- medical-imaging
- segmentation
- pytorch
- deeplabv3plus
- brain-tumor
- mri
library_name: pytorch
---

# Brain Tumor Segmentation — DeepLabv3+ (ResNet-34 + ASPP + WBCE)

An implementation of the architecture and training recipe described in
Soomro et al., *"Boosting Brain Tumor Detection Accuracy in MRI Using
Transfer Learning and Fine-Tuned DeepLabv3+"* (IEEE Open Journal of the
Computer Society, 2026), trained on the CE-MRI brain tumor dataset
(Cheng et al., 233 patients, 3,064 T1-weighted contrast-enhanced axial
slices: glioma, meningioma, pituitary tumors).

This is a personal/academic reproduction project — **not the original
authors' model, not clinically validated, and not for medical use.**
See [Honest results](#honest-results-vs-the-paper) below before using
this for anything beyond learning/experimentation.

## Model architecture

- ResNet-34 encoder, initialized from ImageNet-pretrained weights
- Atrous Spatial Pyramid Pooling (ASPP), rates 12/16/18 + global image pooling
- Lightweight decoder fusing low-level and ASPP features (DeepLabv3+ design)
- Binary output: tumor vs. background, per pixel
- Trained with Weighted Binary Cross-Entropy (WBCE) loss, with per-slice
  adaptive class-imbalance weighting

## Training setup

- 100 epochs, Adam optimizer (β1=0.9, β2=0.999, weight decay 1e-5),
  initial LR 1e-4 with `ReduceLROnPlateau`
- Patient-wise 70/15/15 train/val/test split (163/35/35 patients) —
  no patient's slices appear in more than one split, avoiding data leakage
- Image size 512×512, batch size 8
- Trained on a single Kaggle GPU session (T4×2)

Full training/evaluation code: **[link to your GitHub repo here]**

## Honest results vs. the paper

The paper reports ~98% DSC and ~99.3% sensitivity. This reproduction
falls meaningfully short of that on the two metrics that matter most for
segmentation quality (DSC, sensitivity), while matching closely on
accuracy/specificity — which is expected, since ~98% of pixels in these
images are background, so accuracy/specificity are dominated by the easy
majority class rather than tumor-finding ability.

| Metric | This model (test set, 35 held-out patients) | Paper |
|---|---:|---:|
| DSC (overall) | **75.6%** | 98.0% |
| Sensitivity | 88.95% | 99.3% |
| Specificity | 99.34% | 98.99% |
| Accuracy | 99.11% | 99.1% |

Per-tumor-type DSC: glioma 70.2%, meningioma 85.5%, pituitary 74.6% —
the same relative ordering (meningioma easiest, glioma hardest) as the
paper reports, which is a useful internal consistency check even though
absolute numbers differ.

**Why the gap likely exists** (in rough order of suspected impact):
1. The paper doesn't fully specify some architectural details (exact
   decoder channel widths, output stride choice, precise augmentation
   policy) — this implementation fills those gaps with standard
   DeepLabv3+ conventions, which may not match the authors' exact setup.
2. Training was extended from 40 to 100 epochs and DSC barely moved
   (74.3% → 75.6%), suggesting the model has converged for this
   configuration — the remaining gap is not simply "needs more training."
3. Possible differences in preprocessing intensity (the paper mentions
   CLAHE contrast enhancement; exact parameters aren't given) or data
   augmentation strength.

This gap is reported transparently rather than hidden — reproducing a
paper's exact numbers without the authors' full implementation details
is a known hard problem in ML research, and getting a rigorous, honest
measurement of *how far off* a reproduction is is itself the useful
skill being demonstrated here.

## Intended use

- Educational / portfolio demonstration of a segmentation pipeline
  (transfer learning, class-imbalance-aware loss, patient-wise
  evaluation discipline)
- Starting point for further experimentation (e.g. testing architectural
  variants, better augmentation, longer training with a different LR
  schedule)

## Out of scope / not suitable for

- Any clinical, diagnostic, or medical decision-making use
- Deployment without independent validation on a properly consented,
  IRB-approved clinical dataset
- Use as a certified medical device (it is not one, and has not been
  evaluated as one)

## How to use

```python
import torch
from model import DeepLabV3Plus  # from this repo's model.py

model = DeepLabV3Plus(num_classes=1, use_imagenet_init=False)
state = torch.load("pytorch_model.pt", map_location="cpu", weights_only=False)
model.load_state_dict(state)
model.eval()

# image: torch.Tensor, shape (1, 3, 512, 512), ImageNet-normalized
with torch.no_grad():
    logits = model(image)
    mask = (torch.sigmoid(logits) > 0.5).float()
```

See the [GitHub repo](#) for the full `dataset.py` preprocessing pipeline
(CLAHE + ImageNet normalization) needed to prepare inputs correctly.

## Dataset

Trained on the CE-MRI brain tumor dataset (Cheng et al., 2017,
*"Enhanced performance of brain tumor classification via tumor region
augmentation and partition,"* PLoS ONE). This repository does not
redistribute the dataset — see the original publication for access.

## Citation

If referencing the original paper this reproduces:

```
Soomro et al., "Boosting Brain Tumor Detection Accuracy in MRI Using
Transfer Learning and Fine-Tuned DeepLabv3+," IEEE Open Journal of the
Computer Society, vol. 7, 2026.
```

This repository is an independent reproduction and is not affiliated
with the original authors.