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<title>MedPLIB-BRISC: Joint Brain Tumor Classification and Segmentation</title>
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</head>
<body>

<header class="wrap">
  <h1>Joint Brain Tumor Classification and Segmentation from MRI with an Adapted Multimodal Large Language Model</h1>
  <p class="authors">
    <span>Simon Tong Sing Hee</span><span>Zeng Yi</span><span>Feng Peilin</span>
    <span>Ye Xiaomeng</span><span>Lyu Muyang</span><span>Timothy Aw Bang Hao</span>
  </p>
  <p class="affil">Nanyang Technological University · GP8001 Group Project, Group 3</p>
  <div class="buttons">
    <a class="btn" href="https://github.com/Peilin-FF/Tumor"><svg viewBox="0 0 16 16" aria-hidden="true"><path d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0016 8c0-4.42-3.58-8-8-8z"/></svg>Code</a>
    <a class="btn" href="https://huggingface.co/Sssunset/MedPLIB-BRISC"><svg viewBox="0 0 24 24" aria-hidden="true"><path d="M12 2a10 10 0 100 20 10 10 0 000-20zm-3.5 7a1.5 1.5 0 110 3 1.5 1.5 0 010-3zm7 0a1.5 1.5 0 110 3 1.5 1.5 0 010-3zM12 18c-2.6 0-4.6-1.5-5.3-3.5h10.6C16.6 16.5 14.6 18 12 18z"/></svg>Model</a>
    <a class="btn" href="#demo"><svg viewBox="0 0 24 24" aria-hidden="true"><path d="M8 5v14l11-7z"/></svg>Try the demo</a>
  </div>
</header>

<main class="wrap">

  <section class="narrow">
    <p>Brain tumor MRI analysis asks two questions: which type of tumor is present, and where it is. They are usually answered by a classifier and a separate segmentation network that never share information, so their answers can contradict each other. We adapt MedPLIB-7b-2e, a biomedical multimodal large language model, so that one model answers both questions in one response. It writes the tumor type followed by a special <span class="seg">&lt;SEG&gt;</span> token, and the hidden state of that token is decoded into the tumor mask.</p>
  </section>

  <section class="narrow" id="method">
    <h2>Method</h2>
    <p class="sub">One model, one answer, two outputs.</p>

    <h3>The class and the mask come from one answer</h3>
    <p>Given an MRI slice \(x\) and a fixed instruction \(q\), <a href="https://arxiv.org/abs/2412.09278">MedPLIB</a> writes an answer of the form &ldquo;\(c\) <span class="seg">&lt;SEG&gt;</span>&rdquo;, where \(c\) is one of the four classes. A projector \(P\) maps the hidden state \(h\) of the <span class="seg">&lt;SEG&gt;</span> token to a prompt vector. The <a href="https://arxiv.org/abs/2308.16184">SAM-Med2D</a> mask decoder \(D\) combines this prompt with the image features \(F = E(x)\):</p>
    <div class="eq">\[ S = \sigma\big(D(F, P(h))\big), \qquad \hat{M}_{ij} = \mathbb{I}\big[S_{ij} > 0.5\big]. \]</div>
    <p>The prompt takes the place of the click or box that a user would normally give to SAM. The mask is therefore drawn from what the language model has just decided.</p>

    <h3>The answer is chosen by scoring</h3>
    <p>Only four answers are valid, so we do not parse free text. Each candidate \(a^{(c)}\) is scored by its log-likelihood and the best one is kept:</p>
    <div class="eq">\[ \hat{y} = \arg\max_{c \in \mathcal{C}} \sum_{t=1}^{T_c} \log p_\theta\big(a^{(c)}_t \mid a^{(c)}_{&lt;t}, x, q\big). \]</div>
    <p>The mask comes from the <span class="seg">&lt;SEG&gt;</span> state of the winning answer. No rule removes it afterwards, so an empty mask on a non-tumorous slice has to come from the model itself.</p>

    <h3>Two-stage adaptation</h3>
    <p>Stage A trains only the modules that connect the parts: the image projector, the router, the <span class="seg">&lt;SEG&gt;</span> projector and the mask decoder. Stage B adds LoRA, \(W = W_0 + \tfrac{\alpha}{r}BA\) with \(r = 16\), to all 32 language model layers and switches on the adapters of the SAM-Med2D encoder. It minimizes</p>
    <div class="eq">\[ \mathcal{L} = \mathcal{L}_{\mathrm{CE}} + 2\,\mathcal{L}_{\mathrm{BCE}} + 2\,\mathcal{L}_{\mathrm{Dice}} + \mathcal{L}_{\mathrm{IoU}}, \]</div>
    <p>where the first term trains the answer and the other three train the mask. Non-tumorous slices are trained with an empty mask. Half of the training prompts ask for the class and the mask, a quarter for the class only and a quarter for the mask only.</p>
    <figure>
      <img src="assets/page/train_curves.png" alt="Training losses per epoch">
      <figcaption><b>Training losses per epoch.</b> The answer loss is close to zero after a few epochs, while the mask loss keeps falling through Stage B. Learning the mask is the harder part of the task.</figcaption>
    </figure>
  </section>

  <section id="demo">
    <h2>Ask the model</h2>
    <p class="sub">Pick a test slice. The model scores each of the four valid answers, keeps the most likely one, and draws the mask from that answer's <span class="seg">&lt;SEG&gt;</span> token.</p>
    <div class="demo">
      <div class="thumbs" id="thumbs"></div>
      <div class="viewer">
        <img id="view" alt="Selected MRI slice">
        <div class="cap" id="viewcap"></div>
      </div>
      <div>
        <div class="prompt"><span class="who">Instruction</span>Classify this contrast-enhanced T1 brain MRI as glioma, meningioma, pituitary tumor, or non-tumorous, and segment the tumor region.</div>
        <div class="cands" id="cands"></div>
        <button class="runbtn" id="run">Answer</button>
        <div class="result" id="result"></div>
      </div>
    </div>
  </section>

  <section class="narrow">
    <h2>Results at a glance</h2>
    <div class="table-wrap">
      <table>
        <thead><tr><th>Model</th><th>Accuracy</th><th>Tumor Dice</th><th>Dice, all slices</th><th>False masks</th><th>Contradictions</th></tr></thead>
        <tbody>
          <tr><td>MedPLIB, no adaptation</td><td>0.294</td><td>0.040</td><td>0.085</td><td>63.6%</td><td>7.0%</td></tr>
          <tr class="ours"><td>MedPLIB, unified, ours</td><td>0.980</td><td>0.854</td><td>0.874</td><td>0.0%</td><td>0.0%</td></tr>
          <tr><td>EfficientNet-B0 + U-Net</td><td>0.993</td><td>0.881</td><td>0.812</td><td>61.4%</td><td>9.0%</td></tr>
          <tr><td>EfficientNet-B0 + U-Net, with a hand-written rule</td><td>0.993</td><td>0.881</td><td>0.898</td><td>0.0%</td><td>0.4%</td></tr>
        </tbody>
      </table>
    </div>
    <p class="note">Dice over all slices also scores the 140 non-tumorous slices, where an empty mask counts as correct. The hand-written rule removes the U-Net mask whenever EfficientNet-B0 predicts no tumor. Accuracy of the unified model is about the same on all three imaging planes: 0.980 on axial, 0.984 on coronal and 0.976 on sagittal slices. The full comparison is in the <a href="https://github.com/Peilin-FF/Tumor">README</a>.</p>
  </section>

  <section class="narrow">
    <h2>What adaptation changes</h2>
    <p class="sub">The same model on the same 1,000 test slices, before and after our two training stages.</p>
    <figure>
      <img src="assets/page/confusion.png" alt="Confusion matrices before and after adaptation">
      <figcaption><b>Confusion matrices.</b> Without adaptation, MedPLIB calls almost every slice a meningioma and never answers non-tumorous. After adaptation, all 140 non-tumorous slices are recognized, and the 20 remaining errors are all between tumor types. No tumor is ever called non-tumorous.</figcaption>
    </figure>

    <h3>Real answers before and after adaptation</h3>
    <div class="chats">
      <div class="chat">
        <div class="q"><b>Main instruction, glioma slice</b>Classify this contrast-enhanced T1 brain MRI as glioma, meningioma, pituitary tumor, or non-tumorous, and segment the tumor region.</div>
        <div class="pair">
          <div class="ans"><span class="who">No adaptation</span>Non-enhancing tumor.<span class="tag no">no class, no mask</span></div>
          <div class="ans after"><span class="who">Unified model</span>glioma <span class="seg">&lt;SEG&gt;</span><span class="tag ok">class and mask</span></div>
        </div>
      </div>
      <div class="chat">
        <div class="q"><b>Question it was never trained on, non-tumorous slice</b>Describe this contrast-enhanced T1 brain MRI. Is there a tumor? If so, what type is it and where is it located?</div>
        <div class="pair">
          <div class="ans"><span class="who">No adaptation</span>The tumor appears as areas of increased signal intensity on the image, indicating abnormal growth in the head and neck region.<span class="tag no">invents a tumor</span></div>
          <div class="ans after"><span class="who">Unified model</span>No<span class="tag ok">correct</span></div>
        </div>
      </div>
      <div class="chat">
        <div class="q"><b>Imaging plane question, axial slice</b>Which imaging plane is this brain MRI: axial, coronal, or sagittal?</div>
        <div class="pair">
          <div class="ans"><span class="who">No adaptation</span>axial<span class="tag ok">correct</span></div>
          <div class="ans after"><span class="who">Unified model</span>coronal<span class="tag no">wrong</span></div>
        </div>
      </div>
    </div>
    <p class="note" style="margin-top:12px">The first two cases show the gain from adaptation. The third shows its cost: training on one narrow task made the model lose some general skills, and its free descriptions shrank to a single word. Mixing general question and answer data into training is one way to keep these skills.</p>
  </section>

  <section class="narrow">
    <h2>The model knows when it is unsure</h2>
    <p class="sub">The four answer scores give a confidence for every prediction, at no extra cost.</p>
    <figure>
      <img src="assets/page/confidence.png" alt="Answer confidence">
      <figcaption><b>Confidence of the chosen answer.</b> Correct answers have a median confidence of 0.9997, while the 20 wrong answers average 0.78. Keeping only answers with a confidence of 0.99 or more covers 84.3% of the slices at 99.9% accuracy, with a single error. The expected calibration error is 0.009. In practice, the remaining slices could be sent to a radiologist for review.</figcaption>
    </figure>
  </section>

  <section>
    <h2>Case gallery</h2>
    <p class="sub">For each tumor type, the test slices where the unified model scored its best, median and worst Dice, and the two non-tumorous slices with the largest false masks before adaptation. Nothing is hand picked.</p>
    <div class="tabs" role="tablist" id="tabs"></div>
    <div class="legend"><span><i></i>ground truth</span><span><i class="pred"></i>predicted mask</span></div>
    <div class="colhead"><div>Input</div><div>MedPLIB, no adaptation</div><div>EfficientNet-B0 + U-Net</div><div>MedPLIB, unified, ours</div></div>
    <div id="gallery"></div>
  </section>

  <section class="narrow">
    <h2>Team</h2>
    <div class="team">
      <div class="member"><div class="n">Simon Tong Sing Hee</div><div class="s">Asian School of the Environment</div></div>
      <div class="member"><div class="n">Zeng Yi</div><div class="s">College of Computing and Data Science</div></div>
      <div class="member"><div class="n">Feng Peilin</div><div class="s">School of Electrical and Electronic Engineering</div></div>
      <div class="member"><div class="n">Ye Xiaomeng</div><div class="s">Lee Kong Chian School of Medicine</div></div>
      <div class="member"><div class="n">Lyu Muyang</div><div class="s">School of Mechanical and Aerospace Engineering</div></div>
      <div class="member"><div class="n">Timothy Aw Bang Hao</div><div class="s">School of Social Sciences</div></div>
    </div>
  </section>
</main>

<footer class="wrap">GP8001 Group 3 · Nanyang Technological University · 2026</footer>

<script>
(async () => {
  const LABEL = {glioma: "glioma", meningioma: "meningioma", pituitary: "pituitary tumor", non_tumorous: "non-tumorous"};
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  const esc = s => String(s).replace(/[&<>]/g, c => ({"&": "&amp;", "<": "&lt;", ">": "&gt;"}[c]));
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  // ---- demo ----
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    const ok = d.pred === d.true;
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    thumbs.appendChild(b);
  });
  run.onclick = () => { answered = true; draw(); };
  draw();

  // ---- gallery ----
  const tabs = document.getElementById("tabs"), gal = document.getElementById("gallery");
  const runs = [["zeroshot", "No adaptation"], ["unet_r34_seed42", "EfficientNet-B0 + U-Net"], ["stageB_v2_seed42", "Unified, ours"]];
  const ROLE = {"Best": "Best case for the unified model", "Median": "Median case for the unified model", "Worst": "Worst case for the unified model",
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})();
</script>
</body>
</html>