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manu02/
LAnA-v3

Name: LAnA-v3
Rating: 2.5 (15 reviews)

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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

New users should prefer the standard Hugging Face flow below. The legacy snapshot/manual implementation has been moved to the snapshot-legacy branch for backwards compatibility.

python

import torch
from PIL import Image
from transformers import AutoModel, AutoProcessor

repo_id = "manu02/LAnA-v3"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

processor = AutoProcessor.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModel.from_pretrained(repo_id, trust_remote_code=True)
model.move_non_quantized_modules(device)
model.eval()

image = Image.open("example.png").convert("RGB")
inputs = processor(images=image, return_tensors="pt")
inputs = {name: tensor.to(device) for name, tensor in inputs.items()}

with torch.inference_mode():
    generated = model.generate(**inputs, max_new_tokens=150)

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

Batched inference uses the same path:

python

batch = processor(images=[image_a, image_b], return_tensors="pt")
batch = {name: tensor.to(device) for name, tensor in batch.items()}
generated = model.generate(**batch, max_new_tokens=150)
reports = processor.batch_decode(generated, skip_special_tokens=True)

HF_TOKEN is optional for this public standard-loading path. If you do not set one, the model still loads, but Hugging Face may show lower-rate-limit warnings.

The default main branch is inference-minimal and avoids shipping duplicated component weights.

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-v3
This section describes the completed public training run.
Method: full_adamw
Vision encoder: facebook/dinov3-vits16-pretrain-lvd1689m
Text decoder: gpt2
Visual projection: linear
Segmentation encoder: facebook/dinov3-convnext-small-pretrain-lvd1689m
Image size: 512
Local batch size: 1
Effective global batch size: 16
Scheduler: cosine
Warmup steps: 1318
Weight decay: 0.01
Steps completed: 26352
Planned total steps: 26358
Images seen: 421707
Total training time: 7.7783 hours
Hardware: NVIDIA GeForce RTX 5070
Final train loss: 1.6852
Validation loss: 1.3204

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

HF_TOKEN is optional for this public repo and only helps with Hugging Face rate limits.
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.

manu02/LAnA-v3