Multi-Dataset Multi-Task Learning for COVID-19 Prognosis

A novel Multi-Dataset Multi-Task (MDMT) learning framework that predicts COVID-19 prognostic outcomes from chest X-rays by jointly training on two publicly available datasets with distinct but correlated labelling schemes — AIforCOVID (severity prognosis) and BRIXIA (severity score assessment) — without requiring universal cross-dataset annotations.

Filippo Ruffini · Lorenzo Tronchin · Zhuoru Wu · Wenting Chen · Paolo Soda · Linlin Shen · Valerio Guarrasi

Unit of Computer Systems & Bioinformatics, Università Campus Bio-Medico di Roma · College of Computer Science and Software Engineering, Shenzhen University · Department of Electrical Engineering, City University of Hong Kong

📄 Paper · 🧩 Framework · ⚙️ Setup · 🚀 Usage Guide · 📊 Results · 📚 Citation

Overview

This repository accompanies the paper "Multi-Dataset Multi-Task Learning for COVID-19 Prognosis", accepted at MICCAI 2024 (main conference, poster presentation) and published in the MICCAI 2024 Proceedings (doi: 10.1007/978-3-031-72390-2_24).

We introduce a Multi-Dataset Multi-Task (MDMT) learning framework that leverages two publicly available COVID-19 CXR datasets — each annotated with a different but complementary prognostic signal — within a single unified training pipeline. Our central hypothesis is that jointly learning to assess radiological severity (BRIXIA, task τ₂) enhances the model's ability to classify prognostic outcome groups (AIforCOVID, task τ₁), improving both performance and robustness.

Key contributions:

A MDMT model for two publicly available COVID-19 CXR datasets, each assigned to a distinct task, trained end-to-end with a shared CNN backbone and task-specific fully connected heads.
A multi-task loss function incorporating an indicator function that enables gradient-level disentanglement across heterogeneous data sources.
Extensive evaluation across 18 CNN architectures and 2 validation strategies (5-fold CV and Leave-One-Center-Out), with rigorous statistical analysis, demonstrating significant improvements over STL and fine-tuning baselines.

Framework

Figure 1 — MDMT model architecture. A single batch X is formed by randomly sampling from both datasets D^τ₁ (AIforCOVID) and D^τ₂ (BRIXIA). A shared CNN backbone f^s extracts a common feature representation H, which is routed to two task-specific fully connected heads f^τ₁ and f^τ₂. The indicator function I(Xᵢ, τⱼ) masks out loss contributions from samples not belonging to each task, enabling end-to-end joint optimisation without cross-dataset label requirements.

Multi-Task Loss

$$\mathcal{L} = \sum_{i=1}^{|X|} \mathcal{I}(X_i, \tau_1) \cdot \mathcal{L}^{\tau_1}(O^{\tau_1}_i, Y^{\tau_1}_i) + \mathcal{I}(X_i, \tau_2) \cdot \mathcal{L}^{\tau_2}(O^{\tau_2}_i, Y^{\tau_2}_i)$$

Experimental Configurations

Configuration	Symbol	Description
Single-Task Learning (AIforCOVID)	STL^τ₁	CNN backbone trained on τ₁ alone
Single-Task Learning (BRIXIA)	STL^τ₂	CNN backbone trained on τ₂ alone
Fine-Tuning	FT	STL^τ₂ model fine-tuned on τ₁
Multi-Dataset Multi-Task	MDMT	Proposed: joint training on both tasks

Datasets

Dataset	Task	Symbol	Samples	Centers	Labels	Validation
AIforCOVID (Soda et al., 2021)	Severity prognosis (Mild vs. Severe)	τ₁	1 586	6	Binary	LOCO
BRIXIA (Signoroni et al., 2021)	Severity score assessment	τ₂	4 707	Multiple	4-class	—

AIforCOVID (D^τ₁): 1 586 CXR from COVID-19-positive adults across 6 Italian centres. Patients are classified as mild (home isolation / hospitalisation without ventilatory support) or severe (NIV, ICU, or death). Collected in three successive releases.

BRIXIA (D^τ₂): 4 707 CXR from sub-intensive and intensive care. The Brixia score grades lung opacity in 6 regions (0–3 each). Regional scores are summed and binned into four categories (0–3).

Preprocessing pipeline (both datasets): lung segmentation via U-Net → bounding-box extraction → resize to 256×256 → ImageNet normalisation.

Backbone Architectures

18 CNN backbones across 6 architectural families, all pre-trained on ImageNet:

Family	Models
DenseNet	DenseNet-121, DenseNet-161, DenseNet-169, DenseNet-201
EfficientNet	EfficientNet-b0, EfficientNet-b1-p, EfficientNet-es, EfficientNet-es-p, EfficientNet-lite
GoogLeNet	GoogLeNet
MobileNet	MobileNet
ResNet	ResNet-18, ResNet-34, ResNet-50, ResNet-101, Wide-ResNet50-2
ShuffleNet	ShuffleNet-v2-x0, ShuffleNet-v2-x1

Repository Layout

.
├── configs/
│   ├── common/
│   │   ├── common_multi.yaml           # Shared hyperparameters for MDMT
│   │   ├── common_morbidity.yaml       # Shared hyperparameters for STL^τ₁
│   │   └── common_severity.yaml        # Shared hyperparameters for STL^τ₂
│   ├── bash_experiments/               # JSON files that enumerate backbone sweeps
│   ├── 5/                              # Experiment-specific configs for 5-fold CV
│   │   ├── morbidity/
│   │   ├── severity/
│   │   └── multi/
│   ├── loCo/                           # Experiment-specific configs for LOCO
│   │   ├── morbidity/
│   │   ├── severity/
│   │   └── multi/
│   └── *_debug.yaml                    # Quick-iteration debug configs
│
├── scripts/                            # Single-GPU terminal launchers (run from repo root)
│   ├── preprocess_aiforcovid.sh        # AIforCOVID: DICOM → TIFF → masks + bboxes
│   ├── preprocess_brixia.sh            # BRIXIA: DICOM → TIFF → masks + bboxes
│   ├── train_stl_aiforcovid.sh         # Train STL^τ₁
│   ├── train_stl_brixia.sh             # Train STL^τ₂
│   ├── train_mdmt.sh                   # Train MDMT (proposed method)
│   ├── run_experiments.py              # Batch launcher — runs many backbones locally
│   └── aggregate_results.sh            # Aggregate fold results + statistical analysis
│
├── src/
│   ├── models/
│   │   ├── train_MultiObjectiveModel.py    # MDMT training entry point
│   │   ├── train_morbidity_SingleTask.py   # STL^τ₁ training entry point
│   │   ├── train_severity_SingleTask.py    # STL^τ₂ training entry point
│   │   └── predict_morbidity.py            # Inference script
│   ├── preprocessing/
│   │   ├── AFC/
│   │   │   ├── ClinicalDataCreation_AFC.py # Step 1: DICOM → TIFF for AIforCOVID
│   │   │   └── segmentation_AFC.py         # Step 2: U-Net lung masks + bboxes
│   │   └── BRIXIA/
│   │       ├── convertDicom_brixia.py      # Step 1: DICOM → TIFF for BRIXIA
│   │       └── segmentation_BX.py          # Step 2: U-Net lung masks + bboxes
│   ├── datasets/
│   │   └── organization/               # Dataset iterators and augmentation pipeline
│   ├── postprocessing/
│   │   ├── Create_Final_Report.py      # Aggregate per-fold results into tables
│   │   ├── plot_results.py             # Visualisation of aggregated results
│   │   ├── results_interface.py        # Interactive Dash results explorer
│   │   └── statistical_analysis/
│   │       └── compute_statistical_scores.py  # One-tail t-test comparisons
│   └── utils/                          # Data loading, model builders, training loops
│
├── figures/
│   ├── method.pdf / method.png         # Model architecture figure
│   └── miccai_2024_logo.png
├── requirements/
│   └── requirements.txt
├── Paper-4049.tex                      # Camera-ready LaTeX source
└── supplementary.tex                   # Full per-backbone results table

Setup

1. Clone the repository

git clone https://github.com/fruffini/Multi-Dataset-Multi-Task-Learning-for-COVID-19-Prognosis.git
cd Multi-Dataset-Multi-Task-Learning-for-COVID-19-Prognosis

2. Create the Python environment

python3.9 -m venv covid-env
source covid-env/bin/activate       # Windows: covid-env\Scripts\activate
pip install --upgrade pip
pip install -r requirements/requirements.txt

GPU note. The paper used 4 × NVIDIA TESLA A100 GPUs with batch size 128. For a single GPU with less memory, reduce batch_size in the relevant config file under configs/.

3. Download the datasets

Create the following directory layout at the project root:

data/
├── AIforCOVID/
│   ├── imgs/           ← release 1 DICOMs
│   ├── imgs_r2/        ← release 2 DICOMs
│   ├── imgs_r3/        ← release 3 DICOMs
│   ├── AIforCOVID.xlsx
│   ├── AIforCOVID_r2.xlsx
│   └── AIforCOVID_r3.xlsx
└── BRIXIA/
    ├── dicom_clean/    ← cleaned DICOM files
    └── metadata_global_v2.csv

AIforCOVID — register and download from the AIforCOVID portal or the Zenodo record.

BRIXIA — download from the BRIXIA dataset page (public release).

4. Download the U-Net segmentation weights

The preprocessing pipeline uses a pre-trained U-Net (BSNet) to segment the lungs. Place the weights as follows:

models/
└── segmentation_brixia/
    ├── trained_model.hdf5                   ← used for AIforCOVID
    └── segmentation_brixia-model.h5         ← used for BRIXIA

Weights are available from the BSNet repository.

5. Download cross-validation split files

The fold definitions (CV splits and LOCO assignments) are stored under data/processed/. These are committed to the repository and require no additional steps.

Using the Repository

Step 1 — Preprocess the datasets

Run both preprocessing pipelines once before any training.

# AIforCOVID: converts DICOMs → TIFFs → lung masks → bounding-box manifest
bash scripts/preprocess_aiforcovid.sh

# BRIXIA: converts DICOMs → TIFFs → lung masks → bounding-box manifest
bash scripts/preprocess_brixia.sh

Each script accepts optional path overrides — run with --help for details.

After preprocessing, the directory layout will be:

data/
├── AIforCOVID/
│   └── processed/
│       ├── images/              ← converted TIFFs
│       ├── masks/               ← binary lung masks
│       └── box_data_AXF123.xlsx ← bounding-box manifest consumed by the DataLoader
└── BRIXIA/
    ├── images/                  ← converted TIFFs
    └── processed/
        ├── masks/               ← binary lung masks
        └── box_data_BX.xlsx     ← bounding-box manifest

Step 2 — Train a single model

All scripts are run from the repository root.

STL^τ₁ — Single-Task on AIforCOVID (severity prognosis)

# Default: resnet18, 5-fold CV, release 3
bash scripts/train_stl_aiforcovid.sh

# Custom backbone and LOCO validation
bash scripts/train_stl_aiforcovid.sh \
    densenet121 \
    configs/loCo/morbidity/afc_config_singletask_loCo.yaml \
    3 my_run

# Resume from checkpoint
bash scripts/train_stl_aiforcovid.sh resnet18 \
    configs/5/morbidity/afc_config_singletask_cv5.yaml 3 1 -c

STL^τ₂ — Single-Task on BRIXIA (severity score)

# Default: resnet18, 5-fold CV, global Brixia score
bash scripts/train_stl_brixia.sh

# EfficientNet-b0, per-lung score, LOCO
bash scripts/train_stl_brixia.sh \
    efficientnet_b0 \
    configs/loCo/severity/bx_config_singletask_loCo.yaml \
    brixia_Lung BASELINE_Lung

MDMT — Multi-Dataset Multi-Task (proposed method)

# Default: resnet18, 5-fold CV, global score, release 3
bash scripts/train_mdmt.sh

# DenseNet-201, LOCO, release 3
bash scripts/train_mdmt.sh \
    densenet201 \
    configs/loCo/multi/parallel_config_multitask_loCo18.yaml \
    brixia_Global 3 my_run

# Resume from checkpoint
bash scripts/train_mdmt.sh resnet18 \
    configs/5/multi/parallel_config_multitask_cv5.yaml brixia_Global 3 1 -c

Positional arguments for all train scripts:

Position	STL^τ₁	STL^τ₂	MDMT
1	MODEL	MODEL	MODEL
2	CFG	CFG	CFG
3	RELEASE (1/2/3)	STRUCTURE	STRUCTURE
4	ID_EXP	ID_EXP	RELEASE (1/2/3)
5	`-c` to resume	`-c` to resume	ID_EXP
6	—	—	`-c` to resume

Available STRUCTURE values: brixia_Global · brixia_Lung · regression

Step 3 — Run a full backbone sweep

scripts/run_experiments.py iterates over all backbone × structure combinations defined in a JSON config and runs them sequentially (or in parallel with --max_jobs). Stdout/stderr of each job is written to logs/<job_tag>.log.

# MDMT, 5-fold CV, all 18 backbones — one at a time (single GPU)
python scripts/run_experiments.py -k multi -e 5 -id 1

# STL^τ₁, LOCO
python scripts/run_experiments.py -k morbidity -e L -id 1

# STL^τ₂, 5-fold CV
python scripts/run_experiments.py -k severity -e 5 -id BASELINE

# Run 4 backbones in parallel (multi-GPU or time-multiplexing)
python scripts/run_experiments.py -k multi -e 5 -id 1 --max_jobs 4

# Resume all runs from checkpoint
python scripts/run_experiments.py -k multi -e 5 -id 1 -c

Flag	Description	Default
`-k`	Modality: `morbidity` / `severity` / `multi`	`morbidity`
`-e`	Experiment key (see table below)	`5`
`-id`	Experiment identifier (checkpoint prefix)	`1`
`-c`	Resume each job from its latest checkpoint	—
`--max_jobs`	Max parallel processes	`1`

Experiment key reference:

`-k`	`-e`	Config loaded
`multi`	`5`	`experiment_setups_multitask_parallel_5.json`
`multi`	`L6`	`experiment_setups_multitask_parallel_loCo_6.json`
`morbidity`	`5`	`experiment_setups_morbidity_5.json`
`morbidity`	`10`	`experiment_setups_morbidity_10.json`
`morbidity`	`L`	`experiment_setups_morbidity_loCo.json`
`severity`	`5`	`experiment_setups_severity_5.json`
`severity`	`L`	`experiment_setups_severity_loCo.json`

Step 4 — Aggregate results and run statistical tests

bash scripts/aggregate_results.sh          # release 3, results stored in data_root
bash scripts/aggregate_results.sh 2        # release 2

Summary tables are written to reports/. Statistical comparisons (one-tail t-test, MDMT vs STL^τ₁ and MDMT vs FT) are printed to stdout.

Step 5 — Explore results interactively

python src/postprocessing/results_interface.py
# Open http://localhost:8050

Results

Average performance across all 18 backbones (task τ₁)

Experiment	CV ACC	CV F1	CV GM	LOCO ACC	LOCO F1	LOCO GM
STL^τ₁	66.0	63.9	65.9	61.2	57.3	60.1
FT	67.3	63.1	66.9	64.8	58.9	63.8
MDMT	68.6	66.6	68.5	65.7	64.3	66.0

Statistical significance (one-tail t-test)

Statistic	Comparison	CV ACC	CV F1	CV GM	LOCO ACC	LOCO F1	LOCO GM
Mean (μ↑)	MDMT vs STL^τ₁	***	***	***	***	***	***
Mean (μ↑)	MDMT vs FT	**	***	**	—	—	—
Std (σ↓)	MDMT vs STL^τ₁	*	*	*	*	—	*
Std (σ↓)	MDMT vs FT	—	—	—	*	—	*

* p<0.05, ** p<0.01, *** p<0.001.

Citation

@inproceedings{ruffini2024mdmt,
  title     = {Multi-Dataset Multi-Task Learning for COVID-19 Prognosis},
  author    = {Ruffini, Filippo and Tronchin, Lorenzo and Wu, Zhuoru and Chen, Wenting
               and Soda, Paolo and Shen, Linlin and Guarrasi, Valerio},
  booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
  pages     = {248--258},
  year      = {2024},
  publisher = {Springer Nature Switzerland},
  doi       = {10.1007/978-3-031-72390-2_24},
  url       = {https://link.springer.com/chapter/10.1007/978-3-031-72390-2_24}
}

Please also cite the datasets:

@article{soda2021aiforcovid,
  title   = {AIforCOVID: Predicting the clinical outcomes in patients with COVID-19
             applying AI to chest X-rays},
  author  = {Soda, Paolo and others},
  journal = {Medical Image Analysis},
  year    = {2021},
  doi     = {10.1016/j.media.2021.102216}
}

@article{signoroni2021severity,
  title   = {BS-Net: Learning COVID-19 pneumonia severity on a large Chest X-Ray dataset},
  author  = {Signoroni, Alberto and others},
  journal = {Medical Image Analysis},
  year    = {2021},
  doi     = {10.1016/j.media.2021.101945}
}

Acknowledgements

Filippo Ruffini is a PhD student enrolled in the National PhD in Artificial Intelligence, XXXVIII cycle, course on Health and life sciences, organised by Università Campus Bio-Medico di Roma.

This work was partially supported by:

Italian Ministry of Foreign Affairs and International Cooperation, grant PGR01156
PNRR MUR project PE0000013-FAIR
PNRR – DM 118/2023

Computing resources were provided by NAISS and SNIC at Alvis @ C3SE, partially funded by the Swedish Research Council through grants no. 2022-06725 and no. 2018-05973.

License

Released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

✅ Academic research, teaching, non-profit clinical research, personal study, modification and redistribution with attribution.
❌ Commercial products or paid clinical decision-support systems require a separate licence.

Contact: Valerio Guarrasi — valerio.guarrasi@unicampus.it

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README.md

README.md

Multi-Dataset Multi-Task Learning for COVID-19 Prognosis

Overview

Framework

Multi-Task Loss

Experimental Configurations

Datasets

Backbone Architectures

Repository Layout

Setup

1. Clone the repository

2. Create the Python environment

3. Download the datasets

4. Download the U-Net segmentation weights

5. Download cross-validation split files

Using the Repository

Step 1 — Preprocess the datasets

Step 2 — Train a single model

Step 3 — Run a full backbone sweep

Step 4 — Aggregate results and run statistical tests

Step 5 — Explore results interactively

Results

Average performance across all 18 backbones (task τ₁)

Statistical significance (one-tail t-test)

Citation

Acknowledgements

License

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Multi-Dataset Multi-Task Learning for COVID-19 Prognosis

Overview

Framework

Multi-Task Loss

Experimental Configurations

Datasets

Backbone Architectures

Repository Layout

Setup

1. Clone the repository

2. Create the Python environment

3. Download the datasets

4. Download the U-Net segmentation weights

5. Download cross-validation split files

Using the Repository

Step 1 — Preprocess the datasets

Step 2 — Train a single model

Step 3 — Run a full backbone sweep

Step 4 — Aggregate results and run statistical tests

Step 5 — Explore results interactively

Results

Average performance across all 18 backbones (task τ₁)

Statistical significance (one-tail t-test)

Citation

Acknowledgements

License