LAnA

Layer-Wise Anatomical Attention model

Best current model in this collection: manu02/LAnA-Arxiv

Overview

LAnA is a medical report-generation project for chest X-ray images. The completed project is intended to generate radiology reports with a vision-language model guided by layer-wise anatomical attention built from predicted anatomical masks.

The architecture combines a DINOv3 vision encoder, lung and heart segmentation heads, and a GPT-2 decoder modified so each transformer layer receives a different anatomical attention bias derived from the segmentation mask.

How to Run

Standard AutoModel.from_pretrained(..., trust_remote_code=True) loading is currently blocked for this repo because the custom model constructor performs nested pretrained submodel loads. Use the verified manual load path below instead: download the HF repo snapshot, import the downloaded package, and load the exported model.safetensors directly. You must set an HF_TOKEN environment variable with permission to access the DINOv3 model repositories used by this project, otherwise the required vision backbones cannot be downloaded.

from pathlib import Path
import sys

import numpy as np
import torch
from PIL import Image
from huggingface_hub import snapshot_download
from safetensors.torch import load_file
from transformers import AutoTokenizer

repo_dir = Path(snapshot_download('manu02/LAnA-v2'))
sys.path.insert(0, str(repo_dir))

from lana_radgen import LanaConfig, LanaForConditionalGeneration

config = LanaConfig.from_pretrained(repo_dir)
config.lung_segmenter_checkpoint = str(repo_dir / "segmenters" / "lung_segmenter_dinounet_finetuned.pth")
config.heart_segmenter_checkpoint = str(repo_dir / "segmenters" / "heart_segmenter_dinounet_best.pth")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = LanaForConditionalGeneration(config)
state_dict = load_file(str(repo_dir / "model.safetensors"))
missing, unexpected = model.load_state_dict(state_dict, strict=True)
assert not missing and not unexpected

model.tokenizer = AutoTokenizer.from_pretrained(repo_dir, trust_remote_code=True)
model.move_non_quantized_modules(device)
model.eval()

image_path = Path("example.png")
image = Image.open(image_path).convert("RGB")
image = image.resize((512, 512), resample=Image.BICUBIC)
array = np.asarray(image, dtype=np.float32) / 255.0
pixel_values = torch.from_numpy(array).permute(2, 0, 1)
mean = torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)
std = torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1)
pixel_values = ((pixel_values - mean) / std).unsqueeze(0).to(device)

with torch.no_grad():
    generated = model.generate(pixel_values=pixel_values, max_new_tokens=128)

report = model.tokenizer.batch_decode(generated, skip_special_tokens=True)[0]
print(report)

Intended Use

Input: a chest X-ray image resized to 512x512 and normalized with ImageNet mean/std.
Output: a generated radiology report.
Best fit: research use, report-generation experiments, and anatomical-attention ablations.

MIMIC Test Results

These comparison tables are refreshed across the full LAnA collection whenever any collection model is evaluated.

Cross-Model Comparison: All Frontal Test Studies (`3041` studies)

Metric	LAnA-MIMIC-CHEXPERT	LAnA-MIMIC	LAnA	LAnA-v2	LAnA-v3	LAnA-v4	LAnA-v5	LAnA-Arxiv
ROUGE-L	`0.1513`	`0.1653`	`0.1686`	`0.1670`	0.1745	`0.1675`	`0.1702`	``
BLEU-1	`0.1707`	`0.1916`	`0.2091`	`0.2174`	`0.2346`	`0.2244`	0.2726	``
BLEU-4	`0.0357`	`0.0386`	`0.0417`	`0.0417`	`0.0484`	`0.0441`	0.0503	``
METEOR	`0.2079`	`0.2202`	`0.2298`	`0.2063`	`0.2129`	`0.2002`	0.2607	``
RadGraph F1	`0.0918`	`0.0921`	`0.1024`	0.1057	`0.0939`	`0.0794`	`0.0853`	``
RadGraph entity F1	`0.1399`	`0.1459`	0.1587	`0.1569`	`0.1441`	`0.1437`	`0.1481`	``
RadGraph relation F1	`0.1246`	`0.1322`	`0.1443`	0.1474	`0.1280`	`0.1293`	`0.1308`	``
CheXpert F1 14-micro	`0.1829`	`0.1565`	`0.2116`	`0.1401`	`0.3116`	`0.2196`	0.3552	``
CheXpert F1 5-micro	`0.2183`	`0.1530`	`0.2512`	`0.2506`	`0.2486`	`0.0538`	0.3777	``
CheXpert F1 14-macro	`0.1095`	`0.0713`	`0.1095`	`0.0401`	`0.1363`	`0.0724`	0.1790	``
CheXpert F1 5-macro	`0.1634`	`0.1007`	`0.1644`	`0.1004`	`0.1686`	`0.0333`	0.2647	``

Cross-Model Comparison: Findings-Only Frontal Test Studies (`2210` studies)

Metric	LAnA-MIMIC-CHEXPERT	LAnA-MIMIC	LAnA	LAnA-v2	LAnA-v3	LAnA-v4	LAnA-v5	LAnA-Arxiv
ROUGE-L	`0.1576`	`0.1720`	`0.1771`	`0.1771`	0.1848	`0.1753`	`0.1781`	``
BLEU-1	`0.1754`	`0.2003`	`0.2177`	`0.2263`	`0.2480`	`0.2337`	0.2774	``
BLEU-4	`0.0405`	`0.0449`	`0.0484`	`0.0487`	`0.0573`	`0.0509`	0.0575	``
METEOR	`0.2207`	`0.2347`	`0.2466`	`0.2240`	`0.2310`	`0.2137`	0.2760	``
RadGraph F1	`0.1010`	`0.1000`	`0.1119`	`0.1181`	`0.1046`	`0.0906`	`0.0938`	0.1831
RadGraph entity F1	`0.1517`	`0.1577`	`0.1713`	`0.1739`	`0.1584`	`0.1566`	`0.1580`	0.1831
RadGraph relation F1	`0.1347`	`0.1413`	`0.1549`	0.1628	`0.1405`	`0.1410`	`0.1395`	`0.1596`
CheXpert F1 14-micro	`0.1651`	`0.1442`	`0.1907`	`0.1365`	`0.2921`	`0.2205`	`0.3173`	0.3228
CheXpert F1 5-micro	`0.2152`	`0.1716`	`0.2415`	`0.2455`	`0.2394`	`0.0555`	`0.3372`	0.3745
CheXpert F1 14-macro	`0.1047`	`0.0700`	`0.1039`	`0.0381`	`0.1326`	`0.0714`	`0.1632`	0.2190
CheXpert F1 5-macro	`0.1611`	`0.1112`	`0.1578`	`0.0952`	`0.1636`	`0.0342`	`0.2343`	0.3354

Data

Full project datasets: CheXpert and MIMIC-CXR.
Intended project scope: train on curated chest X-ray/report data from both datasets and evaluate on MIMIC-CXR test studies.
Current released checkpoint datasets: MIMIC-CXR (findings-only) for training and MIMIC-CXR (findings-only) for validation.
Current published evaluation: MIMIC-CXR test split, frontal-only (PA/AP) studies.

Evaluation

Medical report metrics implemented in the repository include RadGraph F1 and CheXpert F1 (14-micro, 5-micro, 14-macro, 5-macro).

Experiment Model Descriptions

LAnA-MIMIC-CHEXPERT: This variant was trained on a combined dataset of CheXpert and MIMIC-CXR using LoRA fine-tuning with the AdamW optimizer.
LAnA-MIMIC: This model was trained on the MIMIC-CXR (findings-only) dataset using LoRA fine-tuning with the AdamW optimizer.
LAnA: This model was trained on the MIMIC-CXR (findings-only) dataset using full-model optimization with AdamW instead of LoRA.
LAnA-v2: This version keeps the same training setup as LAnA, but increases the effective global batch size from 16 to 128.
LAnA-v3: This version keeps the same training setup as LAnA, including the effective global batch size of 16, but changes how EOS is handled so training and generation follow the same behavior. The model no longer uses the EOS token during training, and generation remained greedy without stopping when an EOS token was produced. In the previous setup, decoding was also greedy, stopped at EOS, and used a maximum of 128 new tokens.
LAnA-v4: This version keeps the same decoding behavior as LAnA-v3, but increases the effective global batch size from 16 to 128.
LAnA-v5: This version uses the training recipe from the original LAnA paper, while switching to the legacy CXR-Findings-AI generation behavior.
LAnA-Arxiv: This model is the report-generation model created in the arXiv paper, packaged locally with its original legacy generation code.

Training Snapshot

Run: LAnA-v2
This section describes the completed public training run.
Method: full_adamw
Vision encoder: facebook/dinov3-vits16-pretrain-lvd1689m
Text decoder: gpt2
Segmentation encoder: facebook/dinov3-convnext-small-pretrain-lvd1689m
Image size: 512
Local batch size: 1
Effective global batch size: 128
Scheduler: cosine
Warmup steps: 165
Weight decay: 0.01
Steps completed: 3291
Planned total steps: 3297
Images seen: 421707
Total training time: 8.2334 hours
Hardware: NVIDIA GeForce RTX 5070
Final train loss: 2.0342
Validation loss: 1.8935

Status

Project status: Training completed
Release status: Completed training run
Current checkpoint status: Final completed run
Training completion toward planned run: 100.00% (3 / 3 epochs)
Current published metrics are intermediate and will change as training continues.

Notes

Set HF_TOKEN with permission to access the DINOv3 repositories required by this model before downloading or running inference.
segmenters/ contains the lung and heart segmentation checkpoints used to build anatomical attention masks.
evaluations/mimic_test_metrics.json contains the latest saved MIMIC test metrics.