Coarse Guidance Network (CGN)

• Coarse Guidance Network (CGN) is a module that injects coarse spatial context into high-resolution patch features to enhance slide-level predictions in MIL frameworks.
• It remaps instance features to a coarse grid using field-of-view driven binning and processes them through a lightweight convolutional head to compute a guidance map.
• Empirical evaluations show that incorporating CGNs improves biomarker classification AUCs while maintaining low parameter and computational overhead.

A Coarse Guidance Network (CGN) is a module designed to learn and inject spatial contextual information at a coarser scale into high-magnification instance features within Multiple-Instance Learning (MIL) frameworks for whole-slide image (WSI) analysis. The CGN operates via grid-based remapping of instance features and a lightweight convolutional head to produce a coarse guidance map, which is then used to modulate the instance features before final attention-based aggregation. This approach enables progressive multi-scale context modeling in computational pathology tasks, offering a parameter-efficient mechanism for slide-level prediction enhancement while maintaining computational tractability (Wu et al., 2 Feb 2026).

1. Architectural Overview

The CGN processes high-magnification patch features $H \in \mathbb{R}^{N \times D}$ and their normalized spatial coordinates $(x'_n, y'_n)\in[0,1]^2$. Its core workflow includes three sequential steps:

1. Grid-based Remapping: High-magnification features are aggregated into a 3D coarse feature map $M \in \mathbb{R}^{D \times H' \times W'}$ based on spatial bin assignments determined by a selectable field-of-view (FOV) parameter.
2. Convolutional Guidance Head: $M$ is processed by two 3×3 convolutions with ReLU activations and a 1×1 convolution with Sigmoid activation to yield the coarse guidance map $P \in \mathbb{R}^{1 \times H' \times W'}$.
3. Patch-level Gating: $P$ is flattened and indexed to obtain $M_A \in \mathbb{R}^{N}$, which gates each corresponding row in $H$, resulting in modulated features $H_k = H \odot M_A$.

The diagrammatic ASCII representation is:

H (N×D), coords (N×2) │ ┌───────┴───────────────────────────┐ │ Grid-based remapping │ │ → M ∈ ℝ^{D×H′×W′}, idx∈ℕ^N │ └───────┬───────────────────────────┘ │ ┌───────┴─────────┐ │ Conv3×3(D→D′) │ │ → ReLU │ │ Conv3×3(D′→D′) │ │ → ReLU │ │ Conv1×1(D′→1) │ │ → Sigmoid │ └───────┬─────────┘ │ P ∈ ℝ^{1×H′×W′} │ flatten & index by idx │ M_A ∈ ℝ^N │ H_k = H ⊙ M_A (N×D)

2. Grid-based Remapping

Instance features and coordinates are mapped to a coarse grid via field-of-view driven binning. For each instance $n$ with normalized coordinates %%%%10%%%%, the grid cell assignment $(u_n, v_n)$ is determined as: $$u_n = \left\lfloor x'_n W' \right\rfloor,\quad v_n = \left\lfloor y'_n H' \right\rfloor,\quad \mathrm{idx}_n = v_n W' + u_n$$ where $W' = \lceil W/s \rceil$, $H' = \lceil H/s \rceil$, with $s$ the selected FOV.

The feature map $M$ is computed by averaging all high-magnification vectors falling into each bin: $$M_{c,i,j} = \frac{1}{|G_{i,j}|} \sum_{n \in G_{i,j}} h_{n,c}, \qquad c=1..D$$ where $G_{i,j}$ collects all instances assigned to grid cell $(i,j)$. In vectorized notation,

$$M_\text{sum}[m,:] = \sum_{n: \mathrm{idx}_n = m} h_n,\qquad M[m,:] = \frac{M_\text{sum}[m,:]}{\text{count}[m]}$$

followed by reshaping $M$ to $(D, H', W')$.

3. Convolutional Guidance Computation

After remapping, $M$ is passed through three sequential convolutions: $$U = \mathrm{ReLU}(\mathrm{Conv}_{3\times3}(M; W_1, b_1)) \in \mathbb{R}^{D' \times H' \times W'}$$

$$V = \mathrm{ReLU}(\mathrm{Conv}_{3\times3}(U; W_2, b_2)) \in \mathbb{R}^{D' \times H' \times W'}$$

$$P = \mathrm{Sigmoid}(\mathrm{Conv}_{1\times1}(V; W_3, b_3)) \in \mathbb{R}^{1 \times H' \times W'}$$

Here, $D'=64$ is used as the hidden channel width for all CGN blocks. No self-attention or Transformer module is included; the head is purely convolutional.

$P$ is flattened to length $H' \cdot W'$, and each instance $n$ gathers its coarse guidance value $M_A[n]$ according to its assigned index. The final gated features are $H_k = H \odot M_A$.

4. Integration with Attention-based MIL

In standard attention-based MIL settings, instance embeddings $H \in \mathbb{R}^{N \times D}$ propagate through an attention aggregator $f$ to yield slide-level predictions: $\widehat Y = \varphi(f(H))$ Installing a CGN at scale $s_k$ updates $H$ as: $$H \leftarrow H + H_k,\qquad H_k = (h_1 M_A[1], ..., h_N M_A[N])$$

Stacking multiple CGNs (for example, at FOVs $[1536, 2048, 3072]$) results in a progressive series of residual updates: $$H \leftarrow H+H_1;\qquad H\leftarrow H+H_2;\, \dots$$ The final $H_{\text{mspn}}$ is then input to attention modules such as ABMIL, DSMIL, CLAM-SB, or CLAM-MB, which conduct the slide-level aggregation.

5. Training Protocol and Hyperparameters

Training details for CGN-based models are as follows:

• Losses: Biomarker tasks (ER, PR, HER2 status) use cross-entropy loss. Prognosis tasks (CRC Surv) use a negative log-likelihood loss (NLLSurvLoss) that combines censored and uncensored terms:

$$Y_{\mathrm{hazard}} = \mathrm{Sigmoid}(f(H_{\text{mspn}})),\qquad Y_{\mathrm{surv}} = \prod_t(1-Y_{\mathrm{hazard},t})$$

$$L_{\mathrm{censored}} = -\log Y_{\mathrm{surv}},\qquad L_{\mathrm{uncensored}} = -\log Y_{\mathrm{surv}} - \log Y_{\mathrm{hazard}}$$

$$L_{\mathrm{surv}} = (1-\beta) L_{\mathrm{censored}} + \beta L_{\mathrm{uncensored}}$$

• Optimizer: AdamW, learning rate $2\times 10^{-4}$, cosine-decay scheduler.
• Early stopping: patience = 10.
• Epochs: maximum 150.
• FOV choices: At 20×, e.g., $[1536, 2048, 3072]$ pixels (providing 3 CGNs).
• Hidden channels: $D'=64$ per CGN.
• Parameter and compute cost: Each CGN adds approximately $0.6$M parameters per scale.

6. Empirical Performance and Ablation Results

Empirical studies isolating the CGN demonstrate a consistent benefit on multiple biomarker classification tasks. For instance, a single CGN (FOV=1536) added to ABMIL (using CONCH features) produces:

System	ER AUC (%)	PR AUC (%)	HER2 AUC (%)	Params (M)	FLOPs (G)
ABMIL w/o CGN (single-scale 20×)	87.22	84.14	80.06	—	—
ABMIL + single CGN (FOV=1536)	88.92	84.76	80.84	~1.51	~17.7
ABMIL + three CGNs ([1536,2048,3072])	89.76	85.24	82.86	~2.18	~17.7
ABMIL + five CGNs ([1024,1536,2048,2560,3072])	91.42	84.18	84.62	—	—

Adding at least one CGN leads to a clear increase in slide-level AUC—e.g., gains of +1.70pp (ER), +0.62pp (PR), and +0.78pp (HER2) for a single scale. Stacking multiple CGNs for progressive multi-scale guidance further improves performance (e.g., +4.20pp for ER, +4.56pp for HER2). CGNs achieve these gains at reduced parameter and compute cost relative to methods such as concatenation or cross-scale attention schemes ($2.18$M parameters/$17.7$G FLOPs for CGN vs. $2.61$M/$38.4$G for cross-scale alternatives), while delivering larger accuracy improvements (+3.6pp ER, +4.05pp HER2).

7. Summary of Properties

A CGN remaps high-magnification features to a spatially coarse grid, applies a three-layer convolutional head to compute a coarse attention map, reprojects this map back to the patch level to gate the D-dimensional features, and is trained end-to-end via the same MIL objectives. Each CGN block is lightweight (requiring $D'=64$ hidden channels, $\sim0.6$M parameters per scale), incurs minimal additional computation, and has been shown to consistently improve slide-level prediction performance in clinical biomarker and prognosis tasks (Wu et al., 2 Feb 2026).