update: Add ajustes no README.md

Dockerfile

@@ -11,7 +11,8 @@ ENV PYTHONDONTWRITEBYTECODE=1 \
     TRANSFORMERS_CACHE=/tmp/.cache/huggingface/transformers \
     HUGGINGFACE_HUB_CACHE=/tmp/.cache/huggingface/hub\
     PAPER_URL=https://ieeexplore.ieee.org/Xplore/home.jsp\
-    PAPER_TITLE="RG-DermNet: A Multimodal Attention-Based Model with Residual Block Usage for Skin Lesion Classification \n   PAPER_DESCRIPTION=Skin cancer accounts for nearly one-third of all diag-\nnosed tumors worldwide, making early and accurate recognition\ncritical for improving patient outcomes. In this work, we pro-\npose RG-DermNet, a multimodal deep learning framework that\nintegrates skin lesion images with structured clinical metadata\nthrough a residual gated-attention (RG-ATT) fusion mechanism.\nThe architecture combines CNN- and Transformer-based visual\nbackbones with a lightweight one-hot encoding pipeline for meta-\ndata, enabling effective cross-modal interaction. The proposed\nmodel is evaluated using a patient-wise cross-validation protocol\nacross four dermatological datasets with heterogeneous metadata.\nOn PAD-UFES-20, using Caformer-B36 as the visual backbone,\nRG-DermNet achieves an accuracy of 0.75 ± 0.05, balanced\naccuracy of 0.78 ± 0.03, F1-score of 0.77 ± 0.04, and AUC of\n0.95 ± 0.01, outperforming existing multimodal baselines under\nthe same evaluation setting. In addition, a SHAP-based analysis\nprovides insights into the contribution of clinical metadata to\nthe model’s predictions, supporting both performance gains and\ninterpretability."
+    PAPER_TITLE='RG-DermNet: A Multimodal Attention-Based Model with Residual Block Usage for Skin Lesion Classification'\
+    PAPER_DESCRIPTION='Skin cancer accounts for nearly one-third of all diag-\nnosed tumors worldwide, making early and accurate recognition\ncritical for improving patient outcomes. In this work, we pro-\npose RG-DermNet, a multimodal deep learning framework that\nintegrates skin lesion images with structured clinical metadata\nthrough a residual gated-attention (RG-ATT) fusion mechanism.\nThe architecture combines CNN- and Transformer-based visual\nbackbones with a lightweight one-hot encoding pipeline for meta-\ndata, enabling effective cross-modal interaction. The proposed\nmodel is evaluated using a patient-wise cross-validation protocol\nacross four dermatological datasets with heterogeneous metadata.\nOn PAD-UFES-20, using Caformer-B36 as the visual backbone,\nRG-DermNet achieves an accuracy of 0.75 ± 0.05, balanced\naccuracy of 0.78 ± 0.03, F1-score of 0.77 ± 0.04, and AUC of\n0.95 ± 0.01, outperforming existing multimodal baselines under\nthe same evaluation setting. In addition, a SHAP-based analysis\nprovides insights into the contribution of clinical metadata to\nthe model’s predictions, supporting both performance gains and\ninterpretability.'
 
 RUN useradd -m -u 1000 user
 

src/main.py

@@ -232,7 +232,7 @@ def build_paper_card():
 def build_hero():
     return """
     <div class="hero">
-        <h1>🔬 Multimodal Skin Lesion Explainability</h1>
+        <h1>🔬  RG-DermNet: A Multimodal Attention-Based Model with Residual Block Usage for Skin Lesion Classification </h1>
         <p>
             This interactive scientific demo presents a multimodal skin lesion analysis system that combines
             clinical images and patient metadata to generate predictions and GradCAM++ explanations.
@@ -357,67 +357,6 @@ with gr.Blocks(
     with gr.Column(elem_classes="main-shell"):
         gr.HTML(build_hero())
 
-        with gr.Group(elem_classes="section-card"):
-            gr.Markdown("## 📄 Paper and Scientific Context")
-            with gr.Row():
-                with gr.Column(scale=1):
-                    gr.HTML(build_paper_card())
-                with gr.Column(scale=1):
-                    gr.Markdown(
-                        """
-### Scientific Context
-
-This Space accompanies a multimodal deep learning framework for skin lesion analysis.
-It was designed as an interactive companion to the paper, enabling direct exploration of:
-
-- metadata-aware prediction behavior
-- qualitative GradCAM++ attention patterns
-- differences across pretrained multimodal attention mechanisms
-
-The goal is to make the proposed method easier to inspect, understand, and communicate.
-""",
-                        elem_classes="soft-text",
-                    )
-
-            with gr.Accordion("📘 Full Paper Preview", open=False):
-                gr.HTML(build_paper_embed())
-
-        with gr.Group(elem_classes="section-card"):
-            gr.Markdown("## 🧠 Method Pipeline")
-
-            with gr.Row():
-                with gr.Column(scale=1):
-                    gr.HTML(
-                        """
-                        <div class="pipeline-box">
-                            <h3>Input</h3>
-                            <p>Clinical image and structured patient metadata are jointly provided to the system.</p>
-                        </div>
-                        """
-                    )
-                with gr.Column(scale=0):
-                    gr.HTML('<div class="pipeline-arrow">→</div>')
-                with gr.Column(scale=1):
-                    gr.HTML(
-                        """
-                        <div class="pipeline-box">
-                            <h3>Multimodal Model</h3>
-                            <p>Attention-based fusion integrates visual evidence with metadata-aware reasoning.</p>
-                        </div>
-                        """
-                    )
-                with gr.Column(scale=0):
-                    gr.HTML('<div class="pipeline-arrow">→</div>')
-                with gr.Column(scale=1):
-                    gr.HTML(
-                        """
-                        <div class="pipeline-box">
-                            <h3>Output</h3>
-                            <p>The model returns a diagnosis, confidence information, and a GradCAM++ attention map.</p>
-                        </div>
-                        """
-                    )
-
         with gr.Group(elem_classes="section-card"):
             gr.Markdown("## 🧪 Interactive Demonstration")
             gr.Markdown(
@@ -502,6 +441,16 @@ The goal is to make the proposed method easier to inspect, understand, and commu
                 """
 If you reference this demo in a paper, thesis, or presentation, cite the associated manuscript
 and include the Hugging Face Space as supplementary interactive material.
+
+BibTeX: 
+
+@inproceedings{rocha2026rgdermnet,
+  title     = {RG-DermNet: A Multimodal Attention-Based Model with Residual Block Usage for Skin Lesion Classification},
+  author    = {Rocha, Wyctor F. and Bouzon, Pedro H. G. and Ramos, Lucas A. and Pacheco, Andre G. C. and Souza Jr., Luis A.},
+  booktitle = {International Joint Conference on Neural Networks (IJCNN)},
+  year      = {2026},
+  note      = {Accepted}
+}
 """,
                 elem_classes="footer-note",
             )