Kristin Gleitsman

Leadership in Biotech

Tag: histopathology

Illustration of a desk with a figure from a recent paper about CustOmics

Multimodal CustOmics: Fusing Pathology Images and Tumor Genomics for Next-Gen Cancer Diagnostics

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On September 13, 2025

In AI ∩ Bio

A new paper in PLOS Computational Biology introduces Multimodal CustOmics, a deep learning framework that fuses whole-slide pathology images with tumor molecular profiles (RNA expression, DNA copy changes, methylation, mutations). Why it matters: in oncology diagnostics we already generate both tissue images and sequencing data, but most models treat them separately. This study asks—what if we learn from them together?

Scientific Insight

The authors designed a model that groups molecular signals into gene programs (like “DNA repair” or “immune activation”) and clusters image patches into coherent tissue regions. A fusion layer then learns how programs and patterns align. Across multiple cancer types, the model outperformed existing approaches and even validated on an external lung cancer trial dataset—rare for this field. Interpretability scores trace importance from gene → pathway → tissue region → cell type. It’s compelling, but still correlational: no perturbation experiments to test causality.

Leadership Angle

For diagnostics leaders and investors, the signal is clear: multimodal by design is the next frontier. The advantage is not only higher accuracy but also resilience when some data are missing and structured rationales clinicians can interrogate. The translation challenge will be proving prospective impact—can such a model actually change a clinician’s decision in real time?

AI ∩ Bio

Illustration of scientific paper with the title "AI ∩ Bio: Reading the Revolution"

Mentorship Angle

For early-career scientists: the real craft is not just building complex models, but embedding discipline. Treat interpretability outputs as hypotheses to test, not truths to report. Build the control early—permutation checks, perturbation experiments, site validation. That’s how you transform attention maps into durable scientific insight.

The real test isn’t whether a model like CustOmics outperforms baselines on TCGA. It’s whether, in a prospective trial, it changes a clinician’s decision with confidence and transparency. That’s the bar diagnostics leaders should be watching.

Illustration of a desk with figures from a paper on ConcepPath

From Better Models to Better Questions: A Pathology AI Rethink

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On July 1, 2025

In AI ∩ Bio

This week’s AI ∩ Bio: Reading the Revolution series covers ConcepPath, a new framework that blends expert-derived pathology concepts with deep learning to improve both accuracy and interpretability in histopathology image analysis. Instead of relying only on slide-level labels (e.g., “adenocarcinoma”), ConcepPath uses GPT-4 to extract detailed visual concepts from medical literature and aligns them with tissue regions using vision-language models trained on pathology image–text pairs, helping explain predictions in terms that reflect how diagnoses are taught, documented, and defended in clinical practice.

Scientific Approach

Most AI models for whole slide images (WSIs) use Multiple Instance Learning (MIL); they divide slides into patches, analyze each one, and then aggregate the predictions. This works for classification but offers little insight into why a decision was made. NOTE: more on the ‘standard’ approach in the carousel. ConcepPath adds a critical layer: concept alignment. First, GPT-4 infers visual pathology features from peer-reviewed literature—these become expert-informed concepts. The model then learns additional data-driven patterns directly from the images themselves, potentially novel features that improve prediction even if they lack clinical names. These concepts, both known and learned, are aligned with image features using CLIP-style models (Contrastive Language–Image Pretraining, or models that learn to match images and text that describe the same thing). The model then produces similarity maps showing which parts of the tissue match each concept, for example, highlighting keratin pearls in a region suggestive of squamous carcinoma.

These maps improve interpretability, but they do not replicate diagnostic reasoning. Still, this structured mapping makes model outputs more traceable and aligned with how pathologists evaluate slides.

A Step Forward in Trustworthy AI?

ConcepPath signals a strategic evolution in diagnostic AI—from black-box performance to structured, clinically-aligned transparency. While attention maps and saliency overlays are increasingly common, ConcepPath distinguishes itself by embedding domain knowledge into the model architecture itself. It doesn’t just show where the model looked—it tells us what features it saw.

AI ∩ Bio

Illustration of scientific paper with the title "AI ∩ Bio: Reading the Revolution"

For early-career researchers, this paper is a reminder that innovation doesn’t always come from better algorithms—it often comes from asking deeper questions. In this case, the authors asked: What would it take for AI to reason with the same visual vocabulary and decision cues as a pathologist? That mindset led to a model that’s both potentially better and more explainable. In computational medicine, it’s this pairing of technical skill and conceptual clarity that sets the stage for meaningful impact.

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