single-cell-phenotyping

Single-Cell Phenotyping — Hybrid CNN-ViT Multi-Task Model

Paper: Towards Label-Free Single-Cell Phenotyping Using Multi-Task Learning
Authors: Saqib Nazir and Ardhendu Behera (Edge Hill University, UK)
Conference: ICPR 2026
Code: github.com/saqibnaziir/Single-Cell-Phenotyping

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Model Description

A unified deep learning framework that jointly performs White Blood Cell (WBC) classification and continuous protein-expression regression from label-free Differential Phase Contrast (DPC) microscopy images — without fluorescent staining.

Architecture

Input: (B, 4, 28, 28)  ← 4-channel DPC (Left, Right, Top, Bottom)
    │
    └─ Shared ECA Channel Attention
           ├─ CNN Branch: Stem → Inception×2 + Residual → (B, 196, 192)
           └─ ViT Branch: Patch(4×4) → Transformer×2 → (B, 50, 128)
                  │
                  └─ Cross-Modal Fusion (256-dim, learnable weights)
                         ├─ Task-Specific Refinement
                         └─ Task Gating (bidirectional cross-task exchange)
                                ├─ Classification Head → (B, 3)
                                └─ Regression Head    → (B, 4)

• Parameters: ~12M
• FLOPs: ~0.8 GFLOPs per 28×28 image (real-time capable)
• Input: 4-channel DPC images, 28×28 pixels

Tasks

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Files in This Repository

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Performance

BSCCM Dataset

BCCD Benchmark (Classification Only)

Overall BCCD accuracy: 93.77%

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Usage

Installation

git clone https://github.com/saqibnaziir/Single-Cell-Phenotyping.git
cd Single-Cell-Phenotyping
pip install -r requirements.txt

Load Model

import torch
from huggingface_hub import hf_hub_download
from model import create_model

# Download checkpoint
ckpt_path = hf_hub_download(
    repo_id="saqialii/single-cell-phenotyping",
    filename="bsccm_best_model.pth"
)

# Create model and load weights
model = create_model(num_classes=3, num_proteins=4, img_size=28, in_channels=4)
checkpoint = torch.load(ckpt_path, map_location='cpu', weights_only=False)
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()

print(f"Loaded model — best val accuracy: {checkpoint['best_val_acc']:.2f}%")

Inference

import torch
import torch.nn.functional as F

# Input: (B, 4, 28, 28) — 4-channel DPC image, normalised to [-1, 1]
image = torch.randn(1, 4, 28, 28)  # replace with real image

with torch.no_grad():
    cls_logits, prot_preds = model(image)

# Cell type classification
probs = F.softmax(cls_logits, dim=1)
class_names = ['Lymphocyte', 'Granulocyte', 'Monocyte']
predicted_class = class_names[probs.argmax().item()]
confidence = probs.max().item()

print(f"Predicted: {predicted_class} ({confidence:.1%})")

# Protein expression (Z-scored)
protein_names = ['CD45', 'CD3', 'CD19', 'CD14']
for name, val in zip(protein_names, prot_preds[0].tolist()):
    print(f"  {name}: {val:.3f}")

Data Preparation

# Download BSCCM dataset
pip install bsccm
python -c "from bsccm import download_dataset; download_dataset('./data', mnist=True)"

# Train from scratch
python train.py --data_path ./data/BSCCMNIST --save_dir checkpoints/run1

# Evaluate
python evaluate.py \
    --model_path checkpoints/run1/best_model.pth \
    --data_path ./data/BSCCMNIST \
    --output_dir evaluation_results

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Dataset

BSCCM (Berkeley Single Cell Computational Microscopy):

• 7,889 single-cell DPC images at 28×28 pixels
• 3 WBC classes: Lymphocyte (456), Granulocyte (736), Monocyte (226) — test split
• 4 protein markers measured by fluorescence: CD45, CD3, CD19, CD14
• Source: Waller-Lab/BSCCM

BCCD (Blood Cell Images):

• ~12,000 RGB images at 128×128 pixels (4-class → mapped to 3-class)
• Source: Kaggle: Blood Cell Images

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Citation

@inproceedings{nazir2026label,
  title     = {Towards Label-Free Single-Cell Phenotyping Using Multi-Task Learning},
  author    = {Nazir, Saqib and Behera, Ardhendu},
  booktitle = {Proceedings of the International Conference on Pattern Recognition (ICPR)},
  year      = {2026},
  note      = {arXiv:2605.14717}
}

@article{pinkard2024berkeley,
  title   = {Berkeley Single Cell Computational Microscopy Dataset},
  author  = {Pinkard, Henry and others},
  journal = {arXiv preprint arXiv:2402.06191},
  year    = {2024}
}

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License

MIT License — Saqib Nazir, Ardhendu Behera, Edge Hill University, 2026