Link to paper page

README.md

@@ -1,18 +1,18 @@
 ---
-license: cc-by-nc-sa-4.0
+base_model: OpenGVLab/InternVL2_5-1B
 library_name: transformers
+license: cc-by-nc-sa-4.0
 pipeline_tag: image-segmentation
-base_model: OpenGVLab/InternVL2_5-1B
 tags:
-  - medical
-  - radiology
-  - ct
-  - 3d
-  - tumor-segmentation
-  - referring-segmentation
-  - report-generation
-  - visual-question-answering
-  - custom_code
+- medical
+- radiology
+- ct
+- 3d
+- tumor-segmentation
+- referring-segmentation
+- report-generation
+- visual-question-answering
+- custom_code
 ---
 
 # RepConTUR-1B
@@ -24,7 +24,7 @@ scan.
 
 - **Code:** <https://github.com/Oncosoft-Research/RepConTUR>
 - **Data:** <https://huggingface.co/datasets/Oncosoft-Research/RepConTUR>
-- **Paper:** RepConTUR: A Report-Conditioned Volumetric Multimodal Model for Abdominal Tumor Segmentation (link coming soon)
+- **Paper:** [RepConTUR: A Report-Conditioned Volumetric Multimodal Model for Abdominal Tumor Segmentation](https://huggingface.co/papers/2610.40094)
 
 ## Model
 
@@ -102,10 +102,14 @@ runtime = build_runtime('Oncosoft-Research/RepConTUR')         # model, tokenize
 case = load_case(runtime, 'ct.nii.gz')                 # any 3D abdominal CT in NIfTI
 report_text = open('report.txt').read()                # the narrative report for GSEG
 
-prompt = runtime.image_token_string + '\n' + build_task_prompt(
+prompt = runtime.image_token_string + '
+' + build_task_prompt(
     Task.GSEG, 'tumor in the right kidney', report_text)
 input_ids = runtime.tokenizer.encode(
-    f'<|user|>\n{prompt}<|end|>\n<|assistant|>\n', add_special_tokens=False)
+    f'<|user|>
+{prompt}<|end|>
+<|assistant|>
+', add_special_tokens=False)
 
 output = runtime.model.predict_volume(
     pixel_values=case.pixel_values.cuda(), input_ids=input_ids, g_pixel_values=case.grounding_volume)
@@ -155,4 +159,4 @@ non-commercial use only, and adaptations under the same license.
 
 Built on [Sa2VA](https://github.com/bytedance/Sa2VA), [VoxTell](https://github.com/MIC-DKFZ/VoxTell),
 [CT-CLIP / CT-RATE](https://github.com/ibrahimethemhamamci/CT-CLIP), [InternVL](https://github.com/OpenGVLab/InternVL),
-UniBioMed and AbdomenAtlas 3.0 / RadGPT.
+UniBioMed and AbdomenAtlas 3.0 / RadGPT.