Daily Report — 2026-02-19

Today’s Overview

  • What was done: Three major workstreams were completed in the MIHD project: establishing a technical documentation system (RM-IDEAL bilingual spec, visual encoder guide, pathology PFM literature review, UNI/UNI2 evaluation analysis); a deep survey of H&E Image-Only clustering methods (including quantitative baselines, Foundation Model failure root-cause analysis, and an update to ENHANCEMENT_PLAN_CN.md Goal 7); and full implementation and comparative validation of three image-only self-supervised clustering approaches (STEGO/BYOL+GAT/SCAN).
  • How it was done: Through extensive academic literature search (spEMO, HEST-1k, STAIG, etc.) and deep codebase exploration, created five new model files and modified infrastructure files, ran multi-method comparison experiments on GPU, batch-regenerated UNI2 visualizations from cached .npz files, and compiled all research findings into ENHANCEMENT_PLAN_CN.md.
  • Why it matters: The SCAN method improved image-only ARI from a baseline of 0.251 to 0.303 (+20.6%); fusing its embeddings with gene features yields a further +0.065 ARI. The systematic survey addresses a near-total absence of vision-only spatial domain identification benchmarks in the literature, establishing a reference baseline and a complete multi-stage enhancement roadmap for the project.

In the MIHD project, completed a systematic literature review of H&E Image-Only clustering methods (establishing ARI 0.11–0.16 baselines and five root causes for Foundation Model failure), built four core technical documents, and implemented and validated three self-supervised clustering enhancement approaches (STEGO/BYOL+GAT/SCAN). SCAN improved image-only ARI from 0.251 to 0.303 (+20.6%).

Today’s Tasks

Architecture & Strategy

  • ✅ MIHD Technical Documentation System — Created four core technical documents: RM-IDEAL bilingual structure doc (WWL graph kernel, Wasserstein optimal transport, complementary relationship with ARI/NMI); visual encoder usage guide (12-chapter end-to-end pipeline with detailed comparisons of UNI2/UNI/HIPT/ResNet50); pathology PFM literature review (patch extraction strategies and encoder selection for spEMO/HEST-1k/STAIG, etc.); and a comprehensive analysis of the UNI/UNI2 original paper evaluations (34 clinical tasks + 8 benchmarks).
  • ✅ Systematic Survey of H&E Image-Only Clustering Methods — Surveyed the full landscape of methods including MILWRM, F-SEG, and Deep Contrastive Clustering; specifically verified image-only DLPFC ARI values from ablation experiments (SpaConTDS=0.16, stLearn=0.11, the only two confirmed data points); conducted deep investigation of BYOL/STEGO/SCAN applications in pathology (especially STAIG’s precedent of training an image encoder with BYOL); surveyed cross-domain analogues from FGVC, medical imaging, remote sensing, and materials science; and mapped out the CV community’s four-tier domain gap resolution framework.
  • ✅ Root-Cause Analysis of Foundation Model Failure in Spatial Domain Identification — Systematically analyzed five failure dimensions: training data domain mismatch (predominantly cancer tissue), pretraining task mismatch (patch classification/reconstruction vs. inter-layer gradient recognition), extremely subtle morphological differences between cortical layers, feature redundancy, and lack of spatial context. The UNI2 brown repetitive patch phenomenon was used as a concrete supporting case.
  • ✅ Image-Only Clustering Enhancement Implementation (STEGO/BYOL+GAT/SCAN) — Completed five implementation stages: modified infrastructure (run_benchmark.py, config.yaml, and 3 other files) → created four model files (STEGOHead/BYOLAdapter/SpatialGATRefiner/SCANHead, all validated by AST syntax check) → created eval_image_only.py for comparative testing on section 151673 → updated models/init.py for lazy loading and config integration. SCAN achieved the best ARI=0.303 (baseline 0.251, +20.6%).
  • 🔄 SCAN Embedding + Multimodal Fusion Joint Evaluation — Wrote eval_scan_fusion.py to compare SCAN’s optimized 256-dim visual embeddings against PCA gene features across all fusion methods; mean fusion ARI +0.065, llava_mlp fusion +0.018, confirming complementarity. A coords dimension bug was partially fixed; script debugging ongoing.
  • 🔄 ENHANCEMENT_PLAN_CN.md Goal 7 Update & Image Encoder Enhancement Planning — Wrote all day’s research findings (literature review, root-cause analysis, six solution categories, BYOL deep dive, five-stage implementation roadmap, risk and validation plan) into ENHANCEMENT_PLAN_CN.md (~400 lines expanded to 907 lines). Three parallel agents were also used to analyze the ImageEncoder.py/spatial_utils.py/datasets.py architecture and generate an implementation plan; actual implementation has not yet started.

Implementation & Fixes

  • ✅ Added H&E Panel to UNI2 Visualizations and Batch Updated — Modified the visualization function in scripts/run_benchmark.py to a 1×3 layout (H&E + GT + prediction); fixed the missing tissue_lowres_image.png issue in section 151510 (created a hires→lowres symlink); batch-regenerated all 11 section visualizations from cached .npz files without re-running inference.

Problems & Solutions

Critical Issues

1. STEGO training loss was NaN throughout; model failed to converge

Solution: Two-step fix: ① L2-normalize input image_emb to prevent excessively large magnitudes; ② replace InfoNCE with a numerically stable version (subtract row maximum before logsumexp) and increase temperature to 0.1.

Key Insight: A 3639×3639 dense similarity matrix divided by temperature=0.07 causes exponential overflow under float32 precision; log-sum-exp is the definitive solution and must be used for any large-scale contrastive loss computation.

2. MILWRM was incorrectly classified as an image-only method, and the AI’s first summary mixed multimodal methods into image-only results, requiring a major revision of the initial research findings

Solution: Read the full PMC article via WebFetch to confirm that MILWRM actually relies on gene expression, then removed it from the image-only list. Refocused on pure image settings and specifically mined image-only data points from ablation experiments, guided by two explicit boundary clarifications from the user.

Key Insight: Abstract descriptions in papers can be misleading — reading the full Methods section is required to confirm input modalities. Most multimodal method ablations never test image-only; such data points must be hunted specifically in papers like SpaConTDS.

General Issues

3. Paywalled journals (Nature Medicine, Elsevier, etc.) returning 303/403, and inability to extract exact values embedded in paper figures

Solution: Used PMC full-text mirrors, arXiv HTML versions, HuggingFace model cards, and GitHub READMEs as alternative sources. When figure-embedded values were inaccessible, used qualitative conclusions with explicit source and confidence annotations.

Key Insight: PMC and arXiv HTML are effective workarounds for paywalled journals. Key model performance numbers are often fully listed in GitHub READMEs and should be checked first. When exact values are unavailable, a qualitative conclusion with source annotation is better than estimation.

4. Spatial coordinate dimension anomaly in eval_scan_fusion.py (became (1,2)); multiple fusion methods (basic_contrastive/qformer/staig_fusion) threw errors

Solution: Abandoned load_spatial_coordinates() (which failed due to barcode matching issues) and read coordinates directly from adata.obsm[‘spatial’]; also fixed a return value unpacking error in load_dlpfc_data (function returns a single value, not a tuple).

Key Insight: Utility functions that rely on exact barcode matching tend to fail across data sources. Accessing native AnnData fields directly is more reliable; function signatures should always be verified from source before use.

5. AI repeatedly triggered ExitPlanMode during documentation tasks (rejected twice), and defaulted to CPU for model validation, causing unnecessary interaction friction and efficiency loss

Solution: After explicit user instruction, pure documentation tasks now use the Write tool directly. All model validation was moved to GPU; running three methods in parallel background execution significantly reduced total runtime.

Key Insight: In HPC environments, GPU is the default compute device — CPU testing masks real performance issues. Pure documentation tasks do not require a plan→exit-plan workflow.

6. Needed to regenerate visualizations without re-running UNI2 inference (which takes hours), and section 151510 was missing tissue_lowres_image.png

Solution: Discovered that cached .npz files already contain pred_labels and gt_labels; loaded them directly and called the modified visualization function. All 11/11 sections succeeded. Section 151510 was fixed by creating a hires→lowres symlink.

Key Insight: MIHD’s caching design (embeddings + labels saved together) fully decouples visualization updates from inference. sc.read_visium looks for lowres images by default; using a symlink from hires is the minimal-change solution.

Human Intuition vs. AI Reasoning

Strategic Level

The Fundamental Equivalence of Supervised Classification and Unsupervised Clustering

Role Reasoning
Human The user intuitively pointed out: “Isn’t UNI’s 9-class CRC-100K classification basically just clustering?” — breaking through the paper’s classification framework at a conceptual level, and proactively drawing an analogy between UNI’s evaluation and MIHD’s spatial domain identification.
AI The AI described tasks using the paper’s classification framework (ROI classification/clustering/segmentation/retrieval) without proactively identifying the fundamental connection. Once prompted, it explained the key difference between supervised (linear probe) and unsupervised (KMeans) approaches, and noted this as a literature gap.

Difference: The human broke through the paper framework by reasoning from first principles; the AI stayed within the literature’s descriptive system. This insight was entirely user-initiated — the most important cognitive divergence of the day, revealing that UNI’s high supervised accuracy cannot be directly extrapolated to unsupervised clustering.

Targeted Extraction of Image-Only Quantitative Data from Ablation Studies

Role Reasoning
Human The user proactively asked: “Do these papers have image-only ablation studies?” — this strategy directly located the rare exact numbers like SpaConTDS ARI=0.16.
AI The AI initially searched for standalone papers focused on image-only methods, a correct direction but missing ablation experiments within multimodal papers as a key source of image-only baselines.

Difference: The human had stronger intuition about paper structure (ablation studies typically include modality comparisons) and could target the most productive information source precisely. The AI’s retrieval strategy was more broad-brush and needed user guidance to focus effectively.

Systematic Experimental Design for SCAN Embedding + Full Fusion Joint Evaluation

Role Reasoning
Human After the three-method comparison, the user proactively proposed jointly evaluating SCAN’s optimized embeddings against all fusion methods (including staig_fusion), designing a systematic ablation with a clear two-stage logic: first evaluate visual embedding quality independently, then explore complementarity with gene features.
AI The AI was preparing to wrap up after completing the three-method comparison and did not proactively propose extended experiments; the user’s experimental design showed greater foresight.

Difference: The user had clear experimental design thinking and could independently identify the two-stage logic of independent quality evaluation versus fusion complementarity validation. The AI tends to stop after completing the current goal and lacks the initiative to extend the research scope.

Precise Definition of Research Scope and Visualization Requirements

Role Reasoning
Human The user explicitly redirected the AI twice (“I only want image-only methods/sections”), and proactively proposed adding the original H&E image to visualizations as a morphological reference to intuitively explain the biological meaning of the brown repetitive patch phenomenon.
AI The AI’s first summary habitually provided a full multimodal panorama; in the visualization implementation, it only output a dual-panel GT+prediction layout without proactively suggesting adding the original image.

Difference: Human researchers have clear prior knowledge of research scope and analytical objectives. The AI tends to provide broader context while overlooking constraints; critical detail requirements (morphological comparison) were initiated by the human.

Domain Knowledge Trigger for the BYOL–STAIG Connection

Role Reasoning
Human The user proactively mentioned: “I recall there’s a method that used BYOL” — directing the AI to the key precedent of STAIG using BYOL for unsupervised domain adaptation on target datasets.
AI When compiling the six unsupervised approaches, the AI did not proactively connect BYOL to STAIG’s known usage, listing BYOL as a generic option without highlighting its established practice in the ST field.

Difference: The user’s domain prior knowledge triggered more precise information retrieval. The AI had this connection in its knowledge base but failed to activate it spontaneously — an external cue was needed.

AI Limitations

Significant Limitations

  • Insufficient accuracy in literature classification and knowledge association: MILWRM was incorrectly classified as an image-only method (it actually relies on gene expression) and required full-text WebFetch to self-correct; the analogy between UNI evaluation tasks and MIHD spatial domain identification was not proactively established; the BYOL→STAIG connection was not spontaneously activated when compiling unsupervised approaches. All three cases required user intervention to trigger or correct.
  • Insufficient foresight in technical implementation: The STEGO numerical stability issue (float32 precision boundary for a 3639×3639 dense matrix) was not anticipated at the initial design stage. eval_scan_fusion.py encountered repeated API usage errors (wrong function signatures, incorrect return value unpacking), reflecting a tendency to rely on memory rather than reading source code in real time.
  • Insufficient awareness of task constraints and workflow misjudgment: The first summary ignored the user’s core constraint (image-only), requiring major revision. ExitPlanMode was triggered repeatedly for documentation tasks (rejected twice). CPU was used by default for model validation in an HPC environment. All caused extra interaction friction.

General Limitations

  • Unable to access full text of paywalled journals, and unable to extract specific values from figures or charts embedded in papers (e.g., F-SEG F1 curves, MILWRM DLPFC ARI scatter plots), resulting in gaps in quantitative data that had to be replaced with qualitative conclusions or indirect sources.

Today’s Learnings

Core Takeaways

  • Pure Image-Only achieves only ARI 0.11–0.16 on the fine-grained DLPFC layer identification task (vs. 0.45–0.64 for multimodal), which reflects a combination of extremely subtle inter-layer morphological differences in brain tissue and a Foundation Model training domain mismatch — not encoder quality per se. Multimodal method ablations almost never test image-only in isolation (gene expression is treated as core), which is itself a notable research gap.
  • Five root causes of Foundation Model failure in spatial domain identification: ① training dominated by cancer tissue (domain gap); ② pretraining task mismatch (patch classification/reconstruction vs. inter-layer gradient recognition); ③ extremely subtle morphological differences between cortical layers; ④ high redundancy between image features and gene expression; ⑤ single-patch independent encoding lacks spatial context. The UNI2 brown repetitive patch phenomenon directly reflects root causes ① and ③.
  • SCAN achieves the best image-only ARI for spatial transcriptomics (0.303 vs. baseline 0.251, +20.6%). Its core advantage is that offline feature k-NN mining decouples embedding learning from clustering, avoiding the numerical instability of STEGO. Its 256-dim optimized embeddings are genuinely complementary to gene features (mean fusion +0.065, llava_mlp +0.018 ARI).
  • STAIG’s use of BYOL for unsupervised domain adaptation on target H&E patches (training then discarding the projector/predictor, retaining the encoder features) is a direct precedent for introducing unsupervised domain adaptation into spatial transcriptomics. BYOL’s negative-sample-free design is naturally suited to the small-batch ST setting (thousands of patches per section) and is robust to H&E staining variation.
  • When computing InfoNCE contrastive loss on large dense similarity matrices (n>3000), numerically stable log-sum-exp (subtracting the row maximum) is mandatory; otherwise, exponentiation with temperature=0.07 overflows under float32 precision, causing NaN. This is a critical engineering constraint for large-scale contrastive learning on HPC systems.
  • The CV community’s four-tier consensus framework for handling “domain gap + fine-grained task + no labels”: Level 1 — direct clustering with pretrained features → Level 2 — STEGO/SCAN feature refinement → Level 3 — in-domain SSL repretraining (BYOL/MAE) → Level 4 — dedicated foundation model. The appropriate tier should be chosen based on available compute. GPFM/CHIEF are top-performing PFMs for spatial domain identification ARI; UNI2 is best for spot retrieval; 224×224 is the industry-standard patch size.
  • UNI’s 34 supervised evaluation tasks (linear probe) and MIHD’s unsupervised clustering (KMeans) are fundamentally the same task type but differ in evaluation methodology; UNI’s high supervised accuracy cannot be directly extrapolated to unsupervised clustering performance. HEST-1k demonstrates a log-linear relationship between PFM size and spatial gene expression prediction (R=0.81), with pathology-specific PFMs outperforming ResNet50 by ~8.2% in Pearson r.
  • The spEMO literature review found: GPFM/CHIEF achieve the best clustering ARI for spatial domain identification; UNI2 achieves the best ranking correlation for spot retrieval; 224×224 is the dominant patch size, consistent with MIHD. This provides well-documented literature support for MIHD’s encoder selection.

Session Summaries

✅ Technical Documentation System & UNI2 Visualization Extension (RM-IDEAL / Visual Encoder Guide / Literature Review / UNI2 Evaluation Analysis) 2026-02-19 | claude_code The morning session focused on MIHD technical documentation: created the RM-IDEAL bilingual structure document, a 12-chapter visual encoder usage guide (with UNI2/UNI/HIPT/ResNet50 comparisons), a pathology PFM literature review (spEMO/HEST-1k/STAIG, etc.), and a comprehensive analysis of UNI/UNI2 evaluation tasks from the original papers (34 tasks + 8 benchmarks). Extensive online literature search was used to verify performance data for each method. After the user identified an anomalous brown repetitive patch pattern, UNI2 clustering visualizations were expanded from a dual-panel layout to a three-panel layout including the original H&E image, and all 11 section visualizations were batch-regenerated from cached .npz files (all succeeded after fixing the section 151510 symlink issue).

✅ Deep Survey of H&E Image-Only Clustering Methods & ENHANCEMENT_PLAN_CN.md Goal 7 Update 2026-02-19 | claude_code The early afternoon session systematically surveyed pure-image spatial domain clustering methods: multiple rounds of online search verified image-only DLPFC ARI quantitative baselines (SpaConTDS=0.16, stLearn=0.11). After two user corrections to scope boundaries, focus was precisely narrowed to image-only scenarios. Failure root causes for Foundation Models were analyzed across five dimensions; cross-domain analogues from FGVC, medical imaging, and remote sensing were surveyed; and at the user’s prompting, BYOL’s domain adaptation application in STAIG was examined in depth (the natural advantages of its negative-sample-free design for small-batch ST scenarios). All research findings (~500 lines) were written into ENHANCEMENT_PLAN_CN.md Goal 7, expanding the file from ~400 to 907 lines.

🔄 Image-Only Clustering Enhancement Implementation (STEGO/BYOL+GAT/SCAN) & SCAN Fusion Joint Evaluation 2026-02-19 | claude_code The late afternoon completed five implementation stages: modified infrastructure files → created four model files (STEGOHead/BYOLAdapter/SpatialGATRefiner/SCANHead) → ran GPU-based comparison of four methods on section 151673 (SCAN ARI=0.303 best; all methods running correctly after fixing STEGO NaN loss) → completed integration configuration. Then began writing eval_scan_fusion.py for SCAN embedding + multimodal fusion joint evaluation (mean fusion +0.065 ARI, confirming complementarity), fixing the coords dimension bug. In parallel, three agents analyzed the image encoder enhancement architecture and generated an implementation plan; actual implementation is pending.

Token Usage

Overview

Metric Value
Total Tokens 3,152,997
Input Tokens 10,779
Output Tokens 9,386
Cache Created 399,815
Cache Read 2,733,017
Cache Hit Rate 87.2%
Total Cost (USD) $2.1354

Model Breakdown

Model Input Output Cache Created Cache Read Cost Share
claude-opus-4-6 17 9,258 105,702 914,790 $1.3496 63.2%
claude-haiku-4-5-20251001 10,752 99 228,410 1,515,992 $0.4484 21.0%
claude-sonnet-4-6 10 29 65,703 302,235 $0.3375 15.8%