EviScreen

This is the release code for Evidential Reasoning Advances Interpretable Real-World Disease Screening (ICML 2026).

The repository contains the fundus workflow used for reproduction:

1. prepare fundus CSV files and preprocessed images;
2. build normal/pathological knowledge banks;
3. run the released model directly;
4. train the knowledge banks and evidential reasoning head.

Environment

Create the two Conda environments:

conda env create -f environment-env_bank.yml
conda activate env_bank
pip install -r requirements-env_bank.txt

conda env create -f environment-env_reasoning.yml
conda activate env_reasoning
pip install -r requirements-env_reasoning.txt

Use env_bank for data preparation, knowledge-bank construction, retrieval, and metrics. Use env_reasoning for the Stage 2 evidential reasoning head.

Set local paths once before running the scripts, or edit the same values in constants.sh:

export EVISCREEN_ENV_ROOT=/path/to/miniconda3/envs
export EVISCREEN_RAW_ROOT=/path/to/raw_dataset

Prepare Data

Download the raw datasets and place them under one root: BRSET, EDDFS with annotations, RIADD, and JSIEC

raw_dataset/
  brazilian-ophthalmological/1.0.1/
    labels_brset.csv
    fundus_photos/
  EDDFS/
    OriginalImages/
    train.csv
    test.csv
  RIADD/
    train_set/
    val_set/
    test_set/
  JSIEC/
    1000images/

Preprocess fundus images and generate CSV files:

bash stage_0_data_preparation/preprocess_fundus_images.sh

bash stage_0_data_preparation/generate_fundus_csv.sh

The generated CSV root is:

stage_0_data_preparation/fundus_csv

Prepare Trained Models

Download RETFound_dinov2_meh.pth and place it at:

stage_1_dual_knowledge_bank_construction/ckpt/RETFound_dinov2_meh.pth

For direct reproduction with released EviScreen models, download the trained models from Google Drive and organize them as:

model_for_evaluation/
  normal/
    knowledge_bank_params.pkl
    nnscorer_search_index.faiss
  pathological/
    knowledge_bank_params.pkl
    nnscorer_search_index.faiss
  model.pth

Reproduce Directly

Evaluate the released dual knowledge banks (training-free variant):

bash reproduce_directly/evaluate_model_for_evaluation.sh

Evaluate the released Stage 2 evidential reasoning head (main method):

bash reproduce_directly/evaluate_stage2_direct_retrieval.sh

Outputs are written under reproduce_directly/eval and reproduce_directly/eval_stage2_direct.

Train

Build the dual knowledge banks:

bash stage_1_dual_knowledge_bank_construction/build_dual_knowledge_banks.sh

Train the Stage 2 evidential reasoning head:

bash stage_2_evidential_reasoning/run_stage2_training.sh

After training, evaluate the trained Stage 2 head:

bash stage_2_evidential_reasoning/evaluate_stage2_training.sh

Acknowledgements

Some code and foundation models of this repository are based on PatchCore, RETFound, CheXFound, and PanDerm.