Cancer Survival Analysis via Zero-shot Tumor Microenvironment Segmentation on Low-resolution Whole Slide Pathology Images
Authors: Jiao Tang
WEI SHAO
Daoqiang Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Cancer Survival Analysis，Zero-shot Tumor Microenvironment Segmentation，Low-resolution Whole Slide Pathology Images
Primary area: machine_learning_for_sciences

Abstract: The whole-slide pathology images (WSIs) are widely recognized as the golden standard for cancer survival analysis. However, due to the high-resolution of WSIs, the existing studies require dividing WSIs into patches and identify key components before building the survival prediction system, which is time-consuming and cannot reflect the overall spatial organization of WSIs. Inspired by the fact that the spatial interactions among different tumor microenvironment (TME) components in WSIs are associated with the cancer prognosis, some studies attempt to capture the complex interactions among different TME components to improve survival predictions. However, they require extra efforts for building the TME segmentation model, which involves substantial annotation workloads on different TME components and is independent to the construction of the survival prediction model. To address the above issues, we propose ZTSurv, a novel end-to-end cancer survival analysis framework via efficient zero-shot TME segmentation on low-resolution WSIs. Specifically, by leveraging tumor infiltrating lymphocyte (TIL) maps on the 50x down-sampled WSIs, ZTSurv enables zero-shot segmentation on other two important TME components (i.e., tumor and stroma) that can reduce the annotation efforts from the pathologists. Then, based on the visual and semantic information extracted from different TME components, we construct a heterogeneous graph to capture their spatial  intersections for clinical outcome prediction. We validate ZTSurv across four cancer cohorts derived from The Cancer Genome Atlas (TCGA), and the experimental results indicate that our method can not only achieve superior prediction results but also significantly reduce the computational costs in comparison with the state-of-the-art methods.

Cancer Survival Analysis via Zero-shot Tumor Microenvironment Segmentation on Low-resolution Whole Slide Pathology Images
Jiao Tang, Wei Shao, Daoqiang Zhang The College of Artificial Intelligence, Nanjing University Aeronautics and Astronautics The Key Laboratory of Brain-Maching Intelligence Technology. Ministry of Education tangjiaolmuaa.er edu. cn, shaovei20022005mmaa. edu. .du.cn, cn, dgzhangonuaa.co edu. cn
Abstract
The whole-slide pathology images (WSIs) are widely recognized as the golden standard for cancer survival analysis. However, due to the high-resolution of WSIs, the existing studies require dividing WSIs into patches and identify key compo- nents before building the survival prediction system, which is time-consuming and cannot reflect the overall spatial organization of WSIs. Inspired by the fact that the spatial interactions among different tumor microenvironment (TME) components in WSIs are associated with the cancer prognosis, some studies attempt to capture the complex interactions among different TME components to improve survival predictions. However, they require extra efforts for building the TME segmentation model, which involves substantial annotation workloads on different TME compo- nents and is independent to the construction of the survival prediction model. To address the above issues, we propose ZTTurv, a novel end-to-end cancer survival analysis framework via efficient zero-shot TME segmentation on low-resolution WSIs. Specifically, by leveraging tumor infiltrating lymphocyte (TIL) maps on the 50x down-sampled WSIs, ZTSurv enables zero-shot segmentation on other two important TME components (i.e., tumor and stroma) that can reduce the annotation efforts from the pathologists. Then, based on the visual and semantic informa- tion extracted from different TME components, we construct a heterogeneous graph to capture their spatial intersections for clinical outcome prediction. We validate ZTSurv across four cancer cohorts derived from The Cancer Genome Atlas (TCGA), and the experimental results indicate that our method can not only achieve superior prediction results but also significantly reduce the computational costs in comparison with the state-of-the-art methods.
1 Introduction
Histopathology image analysis a vital technology for cancer survival analysis 1 2]. Traditional methods of survival analysis rely heavily on manual interpretation of these images by pathologists,
which is time-consuming and prone to inter-observer variability. To address these challenges, computational approaches have been explored assist the analysis process. Early computer-aided methods focus on handcrafted features extracted from specific regions of interests (ROIs), which limited in scalability and generalization ability (3). Recently, with the rapid development of the deep learning technology, training deep learning based whole-slide pathology image (WSI) analysis models for cancer survival prediction has gained significant attentions [5. However, the main challenge for survival analysis from the WSIs is that high-resolution WSI is with large size (e.g.,
"Corresponinng author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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100,000-by. 1..00000b--100000 100,000 pixels), and thus it is impractical to directly feed them into deep neural networks due to memory limitations.
To make the analysis of WSIs memory-efficient, most of the existing studies firstly divide WSIs into multiple patches and identify key components before constructing the survival prediction model 66, 1 8 9 TO [II. Then, the patch-level representations are aggregated using attention [[2] or pooling [I3 strategies to predict the clinical outcome. However, dividing high-resolution WSIs into patches is computationally expensive, and the identified key components are insufficient to reflect the heterogeneous tumor microenvironment (TME) components and their spatial associations, As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the TME components around (i.e.. stroma and lymphocyte) [14[5]]
For the purpose of capturing the spatial organizations among different TME components, the existing studies usually adopt graph neural networks (GNNs) to model the interactions among different TME components [16]17] [8] 19 20. 21 [22]. However, these methods require building the TME segmentation model for distinguishing different TME components before graph building, which need extra annotation efforts from the pathologists
Moreover, the existing studies train the TME segmentation and survival prediction models independently, which ignores the fact that the survival information could provide additional information guide the TME segmentation task. For instance, we have more chance to see the TME component of lymphocyte near the tumor region for long survival patients, since it is widely recognized that the brisk interactions between lymphocyte and tumor regions will indicate better clinical outcome [25]. Additionally, the existing GNN-based studies only extract visual features of each TME components as node representations while overlooking their corresponding semantic information, which limits the model's s ability to distinguish different TME components for graph learning.
2
2 2
Based on the above considerations, in this paper, we propose ZTSurv, a novel end-to-end framework for cancer survival analysis through zero-shot segmentation of TME components on low-resolution WSIs. Specifically, instead of working on the high-resolution WSIs, ZTSurv leverages tumor infiltrating lymphocyte (TIL) maps on the 50x down-sampled WSIs to perform pixel-level zero- shot segmentation other two important TME components ( i.e. tumor and stroma) through aatholoyy-languaal foundation model (i.e., PLIP). Then, based on the visual and semantic information extracted from different TME components, we construct heterogeneous graph to capture their spatial intersections for clinical outcome prediction. Extensive experiments on four cancer cohorts derived from The Cancer Genome Atlas (TCGA) validate the effectiveness of ZTSurv, revealing its superior predictive performance and computational efficiency in comparison with the state-of-the-art approaches.
We summarize our main contributions as follows:
We propose a novel end-to-end framework ZTSurv for survival prediction of human cancers that can simultancously segment different TME components and capture their spatial interactions for clinical outcome prediction.
2. We develop zero-shot TME segmentation method that can leverage TIL maps to segment other two TME components (i.e., tumor and stroma) by the aid of pathology-langue foundation model, which can reduce the pixel-level annotation efforts on different types of TME components for constructing the semantic segmentation model.
3. Instead of working on the high-resolution WSIs, we implement our ZTSurv on the 50x down- sampled WSIs that can significantly reduce the computational cost in comparison with the existing studies.
4. We incorporate semantic information alongside the visual features represent each TME compo- nent for graph construction, which can more effectively distinguish different TME components.
Related Work
2.1 Survival Analysis in Gigapixel WSIs
Gigapixel WSIs provide crucial insights for cancer prognosis but are challenging to process directly due to their large size (26). Existing studies typically divide WSIs into multiple fixed-size patches and
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Zero-shot Tumor Microenvironment Segmentation
Heterogeneous Graph Construction for Survival Prediction
[mmpteeste]
Test Pozzae
Vent kambedding
Type
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Thain
PLIP Test Uncodder
Protetype
Madh Decele
Portrce Lidge
Fralee & MIT
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Leng
Risk
Viace Fearre
Securrii Festee
Enooka
RiR
RIS
Vissa Uibbedding
Segrentatton Mark
High-rma high-rmokatee WSI
Visco
WSS1
Figure 1: The overview of our proposed end-to-end framework ZTSurv for survival prediction of human cancers, which can simultaneously segment key TME components and capture their interactions for clinical outcome prediction.
extract useful information from these patches for survival prediction 6 28
For instance, Mobadersany el al. [27] have presented a CNN-based survival prediction model based on the annotated ROIs extracted from WSIs. Zhu el al. [6] exploited and utilized all discriminative
patches in WSIs to predict patients' survival status. Based on ( Yao al. 12) further employed an attention-base based aggregation strategy to fuse informative patches for survival prediction. However, these methods fail capture long-range spatial interactions of different patches in WSIs. To address the above challenges, a bunch of graph learning based survival prediction models are presented. For instance, Li et al. [2] proposed model WSIs graphs and then developed graph convolutional neural network (graph CNN) with attention learning that better served the survival prediction by rendering the optimal graph representations WSIs. Chen et al. [16] presented a spatially- resolved GCN [29] which hierarchically aggregated patch-level features to model local and global topological structures in WSIs. Di el al. [19 proposed a hig-hypergraph factorization neural network to encode the correlation among vertices and hyperedges into two low-dimensional latent semantic spaces for better survival analysis. However, most of the graph learning studies overlook to discuss the interactions among different TME components that are important for cancer prognosis [25]. Also, they usually work on the high-resolution WSIs that will bring significant computational burden.
2.2 Capturing TME Heterogeneity for Survival Analysis
Recent studies have shown that capturing the heterogeneity of the TME is critical for improving the accuracy of survival prediction, as different TME components and their spatial interaction play
important roles for patient outcome prediction 14 30
For instance, the studies in [3 32 23
have attempted to extract the TME information from WSIs for survival prediction. Han et al. (24
introduced a multi-scale heterogeneity-awart hypergraph representation framework to characterize the interactions between different TME components. Wu et al. 3 incorporated the concepts of prototype for TME analysis. Although these methods have shown promising results, they typically require dividing WSIs into patches, followed by training dedicated segmentation classification model, or fine-tuning a foundation model, to identify TME component types and select key patches before constructing graphs. However, such a patch pre-selection process relies heavily additional annotations from pathologists to train a satisfactory classifier or segmentor, which is both labor- intensive and impractical when handling large volumes of high-resolution WSIs. What's more, they often neglect to incorporate semantic features during graph construction, limiting their ability to capture the biological significance of the interactions between different TME components.
Method
Fig. presenns the overview of our proposed ZTSurv. We leverage tumor infiltrating lymphocyte (TIL) maps on the 50x down-sampled WSIs to perform pixel-level zero-shot segmentation other two important TME components (i.e., tumor and stroma). The segmentation results are then used to construct heterogeneous graph, where the different TME components represented as nodes
3

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based on their visual and semantic features. We capture the interactions between these components form the graph edges. The constructed heterogeneous graph updated through graph learning. which ultimately enables the prediction of survival outcomes.
3.1 Zero-shot TME Segmentation
Our objective is segment TME tissue regions that are widely considered be crucial for cancer prognosis (e.g., lymphocyte [33], stroma (30), tumor (34) on low-resolution WSIs using the corre- sponding TIL maps, where only the lymphocyte class is available as ground truth for training. To this end. we first down-sample the high-resolution WSI to match the size of the TIL map with width W' and height H'. We employ the PLIP 33] vision encoder, which divides the image into n patches with patch size of p/, and incorporate a learnable vision prompt to extract visual representations, including the [cls] token E R and vision embedding Z { 22,..., zn E Raxdd where d' is the feature dimension of the PLIP model. Specifically, the input embeddings from the I-th multi-head attention (MHA) module of the ViT-based encoder in PLIP are represented as {g', 21 =2:.... 2 }. We add learnable vision prompt p' processes the tokens as follows (detailed illustrations are available in the Appendix C):
ps.... P', J into the input and the (-th MHA module
lg': zzz Layer' (g' 2.'21_1__ J.
Instead of using generic text prompts like "a photo of O)" we generate class-specific textual descriptions H that capture the appearance attributes of various TME components using large language model (LLM) (details in Appendix D). These descriptions are encoded via the PLIP text encoder to obtain text embeddings H E RCxd" where C' is the number of classes. A projector dpp. composed of three linear layers, maps the text embeddings to prototypes E = ( E RCxd". Inspired by (36), we adopt mask decoder consisting of three layers of lightweight transformers to segment the TME components at the pixel level. The input Q Qquuery), K V (value) are computed as:
(2) where @ is the Hadamard product, By. thx, and 1h, represent linear transformations. At each transformer layer, the query Q is updated to better capture the semantic correlations with the visual embeddings. To obtain the predicted mask, we calculate the score map using scaled dot-product attention from the final layer with a Sigmoid activation to ensure that the segmentation results of each class independently generated:
Q - (E E RCdd', K b= Baxd" V vz) E Baxd
QKT T ddg E RC
ScoreMap = Sigmoid
(3)
where vdk the dimension of the keys as a scaling factor. The score map is then reshaped to C'x (4') x (W'/p') and the final segmentation mask M E ll''' xw' is obtained by applying an Argmax operation along the class dimension followed by an up-sampling step to restore the original spatial resolution. We train the zero-shot segmentation model using focal loss (37] and dice loss (38 39. Notably, only the lymphocyte class, which is visible in the TIL map, contributes to the loss calculation, while other classes are ignored.
3.2 Heterogeneous Graph Construction
To explicitly model the heterogeneity and spatial organization of the TME, we construct a hetero- geneous graph
EV,E, R) based on the predieted segmentation mask M, where V, A,E,A, R represent the set of entities (vertices or nodes), the set of relations (edges), the set of entity types, the space of edge attributes, respectively. Each node E V is associated with type through mapping function (1) E A. An edge = r, t) E È connects a source node to a target node t, where the edge type is given by Φ(e) r R. Each node v has d'dimennsional feature vector x E X, where X' represents the embedding space for node feature. Specifically, we divide the down-sampled WSI into a set of non-overlapping patches using a fixed window size x 8'. For each patch, we determine its tissue label y E A based on the dominant class within the patch area. Patches labeled

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as background are discarded, and the remaining patches are retained as graph nodes V. To construct node features X, we first extract visual features f E RR for cach patch using the PLIP vision encoder. addition, considering the semantic gap and inherent heterogeneity among different TME components, we further obtain semantic embeddings f RR by feeding the corresponding class name into the PLIP text encoder. Then, the final node representation can be calculated as:
x = vis flact E Rdottd 2: E X'.
Here, || denotes the concatenation operation. The node types and node features reflect the biological roles of different TME components and preserve both visual and semantic heterogeneity at the node level. Based on the defined nodes and their feature representations,
establish edges and assign edge attributes to capture neighborhood relationships. For each node v E V, we apply the k-nearest neighbor algorithm identify the & most similar nodes to it, and create directed edges connecting v to each of its neighbors. For each edge e E &, we compute the Pearson correlation coefficient U between the feature vectors of the source and target nodes its continuous attribute, where represents the set continuous edge attributes. The edge type r E R is labeled as "positive" the coefficient is positive and "negative" otherwise. The edge attributes introduce heterogeneity the relational level and help to highlight implicit meta-relations among different tissue regions in the WSI. To reduce potential noise introduced by uncertain spurious correlations, we apply data augmentation strategies during training, including random edge and feature dropout.
3.3 Heterogeneous Graph Learning for Survival Analysis
Traditional graph attention mechanisms fail to effectively address the heterogeneity inherent in the graph structure [3. To address this challenge, we incorporate node features that capture both visual representations and type-specific information, alongside continuous edge attributes, into the aggregation process, which allows the model capture the complex interactions and diverse relationships between different TME components.
Edge Updating, For each edge e E E, we project its continuous attributes w/-1 E U from the (1 1)-th graph learning layer the 1-th layer u' = Wedge u/ using linear projector Wed Wedge:
Node Updating. Instead of using edge similarity weights for node updates, we account for the heterogeneity inherent in both edges and nodes (detailed illustrations are available in the Appendix F). Specifically, in each layer, we update the embedding of node E V by aggregating information from all its neighbors. We refer to node v as target node i, with its neighbors denoted as (()) = {81, 82,.... 8N ). where V(() represents the set of source nodes that point to the target node t, and is the number of neighbors. The set of edges associated with node is denoted as {t) = fei, .,,............................. eN ). At layer I, for each (5%, e,, t) and attention head h, we first project the target node t into a query vector Fh query using linear projector Wh 7(t)= and the source node into key and value vectors Fley. k and Fh value, using W Wh
T(x.)²
Fh query Wh (1)) Fl key. Wh 1-1 FÀ value,
T(s:)
W 7
(5)
where (-1 and x represent the node features of the source node s; and target node at the (I 1) layer, and r(-) A is mapping function that assigns the corresponding type each node, Then we calculate the attention score for each edge on h-th attention head using the query vector Fh query and the key vector Flay, modulated by the edge's continuous attribute u, and apply the Softmax function across all edges in E(t) to obtain the normalized attention scores:
atth (1,e4) ex) Fh key.s
U4 . Fhh Fquery/ dv + des
(6)
w (1,e) e) = moft (1,
(7)
veEE())
where Vd, + di is the scaling factor to ensure numerical stability, w*(t,e) et) E TRN represents the final attention score of the edges associated with target node t on h-th attention head. Finally, the updated
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embedding of the target node is computed by aggregating the weighted value vectors:
Dt (ll hel1.H (t, e;)
F value,
(8)
where || ke]1,H denotes the concatenation of all attention heads. The aggregation process results in an updated feature that effectively combines both node-type and edge-attribute information, thereby capturing the graph's heterogeneity. After completing the L-th layer of graph learning. we employ global attention based pooling [40] to dynamically calculate a weighted sum of the node features in the graph, transforming them into WSI-level embedding representation. The representation is then passed through a MLP to predict the final survival risk score. For the k'-th patient, we can model f Shazard
the survival function Suare (T 2 D(K)) and hazard function the relative clinical information ( (x(*', c,(kk, ,,(K)), where x(k) represents the patient's WSI, c(k') E (0, 1) indicates the censoring status, and y(k() E IRR denotes the overall survival time. After graph learning, we learn the representation f(D(k)) and compute the survival loss Lo (()((()))(())) (())))))))))))))) Ik'=1) of all patients, where ND is the number of samples in the training set. Specifically, we adopt the negative log-likelihood (NLL) loss [4]] to quantify the difference
1/T 2 I, D(K)) given
between the predicted survival risk and the actual clinical outcomes (details can be found in the Appendix B):
No Sarra
log(S (1
(9)
+ (1 g Mog (1((D())))
(10)
+ (1 (k') Nogl ((K))
(oon ((((4))
(11)
3.4 Overall Loss
The overall loss consists of the zero-shot TME segmentation loss Lung and the survival loss Cxures which can be formulated as follows:
C Leeg + Yeurr aC focal + 3Laice + 7Lsurrs
(12)
where {a, 3, } are coefficients that balance the contributions different losses. In Appendix H we further analysis the time complexity of ZTSurv.
4 Experiments
4.1
Datasets
We conduct experiments on four cancer cohorts: Breast Invasive Carcinoma (BRCA) (1,016 cases). Uterine Corpus Endometrial Carcinoma (UCEC) (520 cases). Lung Adenocarcinoma (LUAD) (540 cases), and Bladder Urothelial Carcinoma (BLCA) (369 cases). All WSIs for these cancer types are sourced from The Cancer Genome Atlas (TCGA) repository 42 The corresponding 50x
down-sampled tumor-infltrating lymphocyte (TIL) maps are obtained from 43
4.2 Experimental Settings
Implementation Details. For each cancer cohort, we evaluate model performance using a 5-fold cross-validation strategy. We use openslide [44) tool to process and down-sample the high-resolution WSIs. The WSI segmentation implemented with the open-source MMSegmentation toolbox 45 with PoTch 1.1..1. We employ the pre- rre-trained PLIP VIT-B/32 model to extract visual and textual features, with feature dimension of 512. GPT-4 46 is used as the LLM to generate class
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Table 1: Comparisons of C-index (mean = std) for survival prediction with SOTA methods over four cancer datasets. The best and the recond-best results are highlighted in bold and underlined. p. represents methods sampling patches from WSIs; g. denotes graph-based methods; t. refers to methods considering TME heterogeneity; w. represents methods without splitting WSIs into patches.
Model
Design
BRCA
UCEC
LUAD
BLCA
Overall
CLAM-SB [51
0,581 = 0.041
0,550 : 0.113
0,549 + 0.053
0,563 = 0.049
0.561
CLAM-MB [1
P.
0,541 0.119
0.551 + 0.118
0,556 + 0.067
0.601 + 0.070
0.562
Co-Pilot 22
p-
0.544
0.076
0.557
0.112
0,571 + 0.083
0.587 = 0.056
0.564
DeeLAttMMSSL. 12
p.
0.598 0.066
0.639 = 0.068
0.601 = 0.041
0.597 = 0.028
0.609
DSMIL 11
p.
0.548 0.080
0.581 0.164
0.538 0.047
0.552 0.052
0.555
WSISA 6
p.
0,514 0.071
0.539 + 0.114
0.575 0.055
0.581
0.050
0.552
DeepGraphSurv
21
p.tg-
0.587
0.033
0.622 + 0.097
0.595 + 0.011
0.584 = 0.037
0.597
HGSurvNet 22
p.tg.
0.624
0.093
0,614 + 0.034
0.587 + 0.045
0.578
0.041
0.601
PatchGCN [6
p.tg.
0.608 0.043
0.678 + 0.128
0.614 + 0.007
0.599 = 0.034
0.625
H2GT 24
p.+g-+t.
0.618
0.032
0.671 0.096
0.629 0.046
0.623
0.058
0.635
TMEGL 2
p.
0.623
0.017
0.700 = 0.053
0.631 0.023
0.627
0.029
0.645
ProtoSurv 3
p.+g.+t.
0.625
0.009
0.705 0.131
0.638 0.026
0.629
0.043
0.649
ZTSurv (ours)
w.+g.+t.
0.642 0.029
0.726 0.113
0.637 + 0.033
0.637 0.042
0.661
P value:
p value:
p valae:: 7.41e
value:
BRCA
UCEC
LUAD
BL.CA
Figure 2: Kaplan-Meier curves for predicted high-risk (red) and low-risk (green) groups across four cancer datasets. A p-value < 0.05 indicates statistical significance.
descriptions for different tissue categories. For graph construction, we set the window size s' = 64
and select 8 neighbors per node. Data augmentation is applied by randomly dropping node
and edge features with a dropout rate of 0.2. The layer for graph learning L is set to 2, with H = 4
attention heads. The hyperparameters for the loss function are set as 20, B 1, and ς 20. We use the AdamW optimizer [47] with learning rate of 2 x 10-5 and weight decay of 1 x 10 10-5, The batch size is set 8, and we train the model for 2K iterations. detailed parameter analysis is provided in the Appendix H.5
Evaluation Metrics. To evaluate TME segmentation performance, we use the mean of class- wise Intersection over Union (mIoU) on lymphocyte, i.e., the only TME component with available ground truth annotations, to measure the overlap between predicted and reference regions. To assess predictive performance, we use the concordance index (C-index) [48] for evaluating the ability to correctly rank the survival risk of different patients. For qualitative assessment, employ Kaplan- Meier curves [49] with log rank test [50] to visualize patient stratification, distinguishing between low and high-risk patients with two separate survival distributions. More details can be found in Appendix G
4.3 Comparison with State-Of-The-Art Methods.
We compare our proposed method with several state-of tfate-of-the-ate art approaches for survival prediction: (1)
CLAM-SB [5T) (2) CLAM-MB [5T). (3) Co-Pilot 28). (4) DeepAttMISL [12. (5) DSMIL [I]), (6)
WSISA 6 (7) DeepGraphSurv 21]. (8) HGSurvNet (2). (9) PatchGCN [6]. (10) H2GT 24. (11)
TMEGL 22) (12) ProtoSurv 3. Among them, CLAM-SB, CLAM-MB, Co-Pilot, AnnSSL,
DSMIL,, and WSISA are classical WSI survival prediction approaches; DeepGraphSury, HGSurvNet, and PatchGCN model spatial relationships by constructing homogeneous graphs for survival predic- tion; H2GT, TMEGL, and ProtoSurv leverage the heterogeneity of the TME for survival analysis. All
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Table Comparisons of mloU (mean std) for TME segmentation with SOTA methods over four cancer datasets. The best and the second-best results are highlighted in bold and underlined.
Model
BRCA
UCEC
LUAD
BLCA
Overall
ZegFormer 52
0.536 = 0.109
0.459 = 0.125
0.566 0.021
0.561 0.047
0.531
ZegCLIP
0.547 0,105
0.475 0.121
0,572 0.040
0,570 0.055
0.541
TagCLIP
0.541
0.474 0.141
0.568 0.036
0.579 0.059
0.541
ZTSurv-Seg
0.552 = 0.096
0.478
0.150
0.579 = 0.050
0.589 + 0.056
0,550
ZTSurv (ours)
0.574 0.023
0.506 0.147
0.023 0.595
0.608 0.055
0.571
(BRRRC)AALLLBRCA)
hmpghrrte
Strozto
numor
CONCIE:
CONCH:
lympheoye
CONCH
PLIP
tumer
PLIP:
Figure 3: Zero-shot TME segmentation comparison of ZTSuuv, CONCH, and PLIP.
these methods require splitting WSIs into patches essential processing step, while ZTSurv can avoid this step and directly utilize low-resolution WSIs for training an end-to-end survival prediction model.
As shown Table ZTSurv outperforms all classical [1 28
6 and graph-based 21 22 16
methods by margin of 1.8%-4.8%, demonstrating the effectiveness of our method that incorporates TME heterogeneity for more accurate survival prediction. Compared to recent TME-aware models such H2GT (24, TMEGL 22] and ProtoSurv [3, ZTSurv still achieves the highest C-index out of cancer datasets, with an improvement of 0.8%-2. %. These results highlight the strength of ZTSurv that can simultaneously realize the TME segmentation and survival prediction tasks, while the existing studies treat these two tasks independently that miss the inherent correlation among them.
4.4 Patient Stratification
perform Kaplan-Meier analysis by separating patients into high-risk and low-risk groups based on predicted risk scores, using the median value within each validation set as the cut-off. The log-rank test [50] is then applied to compute p-values that assess the statistical significance of survival differences between the groups, with smaller p-values indicating better stratification. In Fig. 2 and Appendix H.I we present the stratification ability of our method across four cancer datasets and compare it with two best competitors (i.e., ProtoSurv and TMEGL). The results clearly show that our method achieves more distinct separation between high-risk (red) and low-risk (green) groups across all cohorts with lower p- p-values, which further demonstrates the effectiveness of our method.
4.5 Ablation Study and Analysis
Component Ablation. To further investigate the contribution of each component in ZTSurv, we conduct an ablation study by removing or replacing key modules Appendix H.2 1) w/o text prompt: Removing text prompt of zero-shot segmentation. 2) w/o vision prompt: Removing vision prompt of zero-shot segmentation. 3) w/o node type: Constructing homogeneous graph without considering node types during graph construction. 4) w/o semantic feature: Without considering semantic feature for node representation during graph construction. 5) w/o vision feature: Without considering vision feature for node representation during graph construction. 6) w/o edge attribute: Without considering edge attribute during graph construction and learning. As can be observed from Table in Appendix, ZT'uuv is superior to its variants, indicating that each component of our method effective improving survival prediction performance.
Comparison of Zero-shot TME Segmentation. We compare the zero-shot TME segmentation performance with several state-of-the-art methods: (1) ZegFormer [52), (2) ZegCLIP (53. (3)

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Table 3: Effect of tissue category choices on C-index (mean std) over four cancer datasets.
UCEC
Overall
lymphocytc
0.628 0.020
0.713 + 0.128
0.629 0.06
0.629 0.047
0.650
lymphocyte stroma
0.634 0.043
0.704 + 0.056
0.633 +: 0.065
0.623 0.049
0.649
lymphocyte tumor + stroma
0.642 0.029
0.726 + 0.113
0.637 0.033
0.637 0.042
0.661
503
130
Mesooy
2T5
100
0.25
30-
BRCA
UCBC
1UAD
BLCA
0
(a) Average Memory Usage per WSI.
(b) Average Inference Time per WSI.
Figure 4: Comparison of Average Memory Usage and Inference Time per WSI.
TagCLIP [54].
We also evaluate variant our method, ZTSurv-seg, which is trained solely with segmentation loss and without survival-guided information. As shown in Table ZTSurv consistently outperforms all compared methods across four cancer datasets, achieving the highest overall mloU of 0.571. Additionally, compared to its variant ZTSurv-seg, which lacks survival guidance, ZTSurv shows consistent gains in segmentation performance across all cohorts. These results suggest that incorporating survival supervision provide informative signals that help refine the segmentation process, resulting in more prognostically meaningful TME delineation. Fig. 3 we further present the visualization results of segmentation and compare it with CONCH (55) and PLIP (35) As shown in Fig. 3] our method accurately captures key TME components, while existing survival analysis approaches that rely on pathology foundation models, like CONCH and PLIP, often struggle to identify non-tumor components, limiting their ability to fully capture TME heterogeneity. The results further highlight the effectiveness of our approach in zero-shot TME segmentation, enabling more comprehensive and precise survival analysis. In the Appendix H.4 we provide more visualization results on other three datasets (i.e,, BLCA, LUAD, and UCEC).
Comparison of Survival Prediction with Different Zero-shot Classifiers. In Appendix H.3 we compare ZTSurv with three alternative approaches for the zero-shot TME segmentation stage (i.e., CONCH (55, PLIP 35,, and a UNI classifier [56) finetuned as described in (3) for survival outcome prediction. As shown in Fig. in Appendix, ZTSurv consistently achieves superior C-index scores across all four cancer datasets, demonstrating that our method achieves better performance.
Effect of Different Tissue Categories. In our work, we consider three key TME components, (ie., lymphocyte, tumor, and stroma), to capture the complex TME. further investigate the impact of different tissue categories survival prediction, we conduct experiments with different combinations of tissue components (i.e., lymphocyte + tumor, lymphocyte stroma) to evaluate their influence on survival analysis. As shown in Table 3 including more comprehensive set of tissue categories (i.e., lymphocyte + tumor stroma) consistently achieves the highest C-index across four datasets, indicating that capturing broader TME leads to better survival prediction.
Time and Memory Analysis. Compared with conventional patch-based methods that divide high- resolution WSIs into thousands of tiles, ZTSurv offers significant advantages in both time and memory efficiency. As shown Fig. 4 on a single NVIDIA RTX 4090 GPU, ZTSurv reduces average memory usage per WSI by approximately 3,000 times and decreases average inference time by about 6 times compared other methods. This primarily because ZTSurv directly processes down-sampled WSI without the need to generate and store thousands of small patches, significantly reducing computational overhead and memory requirements, which further demonstrates superior scalability and efficiency for large-scale whole-slide image analysis.

---

Conclusion
In this paper, we propose ZTSurv, a novel end-to-end framework for cancer survival analysis that integrates survival prediction with zero-shot TME segmentation low-resole wewouusottion WSIs. ZTSurv eliminates the need for manual annotations by leveraging TIL maps to segment key TME components, and introduces survival-guided segmentation enhance the identification of prognostically TME regions. By performing segmentation directly on 50x down-sampled WSIs, the framework signif- icantly reduces computational cost. Furthermore, the incorporation of semantic features in graph construction allows the model to better capture complex tissue interactions. Extensive experiments on four TCGA cohorts demonstrate the effectiveness and efficiency of ZTSury, indicating its strong potential for scalable and practical histopathology analysis.
Acknowledgment
This work was supported by the National Natural Science Foundation of China (Nos. 62136004, 62272226, 62102188) Key Research and Development Plan of Jiangsu Province, China under Grant BE2022842, National Key R&D Program of China (Grant No. 2023YFF1204803).
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A Overview
In this appendix, we first provide detailed analysis of survival prediction in B We then provide the detailed illustrations of vision prompt and text prompt for zero-shot TME segmentation in and D respectively. In E and F we present the detailed illustrations of mask decoder of zero-shot TME segmentation and node updating in graph learning, respectively. Then, we provide the evaluation
metric for survival prediction in In H we present more experiments and analysis. including comparisons of patient stratification H.1
ablation study of main components H.2 comparisons
of survival prediction with different zero-shot classifiers H more zero-shot TME segmentation
visualization results H.4, parameter analysis H.S] time complexity analysis H.6 and discussions of
limitations and future work H.7 Finally, discuss the potential ethical issues which may arise in our study in !
B Survival Analysis
Survival prediction aims to model the time until an event occurs, typically formulated as an ordinal regression problem. clinical data, the event (e.g., death) not always observed due censoring. such as when patient is lost to follow-up. up. These right-censored cases introduce uncertainty, as we only know that the event did not occur before the last recorded time.
Following our notation in Sec. 3.3 let D = (X, e,,) represent the patient's clinical data, where X is patient's s pathology image, 1 IR denotes the overall survival time, c E (0, specifies whether the data is right-censored. We denote 7 as continuous random variable representing survival time. The survival function feurv 2 t, D) estimates the probability of patient surviving beyond time while the hazard function [hasera(t) occurring at time given survival up that point, which is defined as:
Shasard t/T' 2 f, D) quantifies the risk of the event
P(t < T < t 0t T t)
fnozard =t) lim 0t-+0
(13)
0L
which can be used to estimate fauru (T 2 t, ((")) by integrating the hazard function fhazard- The most common model for learning hazard functions is the Cox Proportional Hazards (CoxPH) model, where the hazard is modeled as:
frazard = fhazara(t) exp(0" D).
(14)
Here, fhazard( (t) is the baseline hazard, and 0 contains parameters that modulate risk based on the input features D. deep learning applications, 0 typically produced by the final layer of neural network and is optimized using the partial log- likelihod of the Cox model via stochastic gradient descent.
g
[els] token
Cony
Layer
patch embedding
position embedding
Z
learaale prompt
Figure 5: Illustration of vision prompt for zero-shot TME segmentation.
C
Vision Prompt of Zero-shot TME Segmentation
In Sec. 3.1 we use the vision prompt, shown in Fig. 5 to enhance the extraction of visual features from low-resolution WSIs. The [els] token, patch embeddings, and learnable prompts are passed through each layer, with only the learnable prompts being trainable.
22

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Score Map
Mask Decoder x
[cls] token g
Sigmto
Prototype
Sclf- Self-Atte Attittttioon
Add & Norn
Scale -
Add Norm
Add & Noma
MarMal
Softmax
MalMal
ML
Update 0
linear
linear
lincar
Vision Embedding Z
Figure 6: A detailed illustration of mask decoder in zero-shot TME segmentation.
Text Prompt of Zero-shot TME Segmentation
In Sec. 3.1 generate class-specife textual descriptions which capture the appearance attributes of TME components using the LLM. Specifically, instead of using generic prompts like "a photo of (1", use the following prompts fed into the LLM: "Describe // in the pathology image in detail, including features such as color and shape". We list the textual descriptions for key TME components (i.e., lymphocyte, stroma, tumor) follows:
This is the lymphocyte in the pathology image, typically appearing as small, round cells with light blue cytoplasm and dark purple nuclei.
"lymphocyte":
"stroma": This is the stroma in the pathology image, typically appearing as fibrous tissue in pale pink or beige tones, supporting the surrounding cells.
"tumor": This the tumor the pathology image, typically appearing as irregularly shaped cells with darker purple or blue cytoplasm and enlarged, pleomorphic nuclei.
E Detailed Illustrations of Mask Decoder
In Fig. 6 we present a detailed illustration of the mask decoder inspired by [36 in zero-shot TME segmentation, which progressively refines feature representations to generate accurate segmentation masks.
Detailed Illustrations of Node Updating
In Fig. 7 we provide detailed illustration of node updating in graph learning, where the target node is solely associated with source nodes A and B in this example.
G Evaluation Metric for Survival Analysis
We use the concordance index (C-index) to evaluate the predictive accuracy of survival analysis models, which evaluates a model's ability to correctly rank pairs of samples based on their predicted risk experiencing an event (e.g., death) within given time frame. Specifically, samples are sorted by their predicted survival scores, and the C-index reflects the proportion of correctly ordered pairs. The metric is defined as:
N
1
C-index N'(N' 1)
I(T, < TT)(1 - cj),
(15)
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Edge Attribate
Source Node
Target Node
Aim: Updatc the target node based on information from the source nodes.
Souree Node A
Edge Attribuic a
Edge Attribute
Edge Attributte
Target Node
Target
Source Node
Lineur
Source Node B
Softmax
Update
Figure 7: A detailed illustration of node updating in graph learning.
where N' is the total number of patients, and the indicator function that returns when the condition holds and otherwise.
H
More Experiments and Analysis
H.1 Patient Stratification
In addition to evaluating prognostic performance using the C-index, patient stratification is another key aspect of cancer survival analysis, enabling the identification of subgroups with distinct clinical outcomes for personalized treatment. We compare the patient stratification ability of ZTSurv with its two best competitors (i.e., ProtoSurv and TMEGL) Fig. g and the results demonstrate that ZTSurv consistently achieves clearer separation between risk groups, indicating more accurate prognosis.
H.2 Component Ablation
Table 4 shows the ablation results of ZTSurv on C-index across four cancer datasets. As shown in Table 4 removing either the text prompt or vision prompt in TME segmentation results noticeable drop in performance across all datasets, with the overall C-index decreasing 0.644 and 0.650 respectively, confirming the effectiveness prompt-based guidance for zero-shot seg- mentation. In terms of graph construction, feature representations. It is clear that removing node type information, i.e., treating all nodes as homogeneous, leads a noticeable performance drop, underscoring the importance preserving semantic distinctions among TME regions. Furthermore, excluding the vision feature results in the most significant degradation, indicating that visual cues derived from WSIs are eritical for accurate survival prediction. The absence of semantic features also degrades performance, suggesting their complementary value in capturing contextual information. Additionally, eliminating edge attributes weakens the model's s ability to capture spatial relationships, resulting in further drop in the overall C-index to 0.651. The results emphasize the critical role of each component in our framework.
further examine the influence of node types and
H.3 Comparison of Survival Prediction with Different Zero-shot Classifiers
In Fig. we compare ZTSurv with three alternative approaches for the zero-shot TME segmentation stage (i.e., CONCH (55), PLIP (35], and a UNI classifier (56) finetuned as described in (3] to predict

---

p-valle: 2.50e-44
p-talaz: 9-11-05
p-iauc 741o.0
p-tiloe: 721c.0
Z.Turry (ours)
2.54p-0
122ex02
p-valee 2.40--02
ProteSurv
TiM
2.2600
Taa paoalar: 16ee03
34g-02
2.20-113
TMEGL
Oven
LUAD
Figure 8: Kaplan-Mal Meier curves for predicted high-risk (red) and low-risk (green) groups across four cancer datasets under different comparative methods. A p-value < 0.05 indicates statistical significance.
Table 4: Ablation study of ZTSurv on C-index (mean + std) over four cancer datasets.
BRCA
UCEC
LUAD
BLCA
Overall
w/o prompt
0.618 0.021
0.706 0.098
0.631 0.035
0.622 0.018
0.644
w/o vision prompt
0.621 0.016
0.723
0.089
0.623 0.026
0.631 0.027
0.650
w/o node type w/o semantic feature
0.615 0.025
0.702 0.110
0.624 0.019
0,630 0.043
0.643
0.624 0.023
0.701
0.071
0.635 0.015
0.624 + 0.026
0.646
w/o vision feature
0.611 + 0.026
0.669 0.075
0.625 0.040
0.621 + 0.023
0.632
w/o edge attribute
0.632 0.024
0.707 0.072
0.632 0.036
0.632 0.006
0.651
ZTSurv
0.642 0.029
0.726 0.113
0.637 0.033
0.637 0.042
0.661
the survival outcome. The results further demonstrate the effectiveness of our method in capturing TME heterogeneity.
H.4 More Zero-shot TME Segmentation Visualization
In Fig. 10 we provide more zero-shot TME segmentation visualization results on BLCA, LUAD, and UCEC cancer datasets. The results demonstrate that Z.TSrrv can capture key TME components more accurately.
H.5 Parameter Analysis
We analyze the sensitivity of the number of neighbors & used for connecting nodes and the window size for node generation. As shown in Table k = 8 yields the best overall performance, while both smaller (k- = 4) and larger (k 16) values result in performance degradation. For the window size s', we observe that s' sizes result in overly complex graphs with redundant information and larger sizes fail to capture sufficient local detail, which highlights the importance balancing graph density and information granularity in node construction.
achieves the highest C-index across all datasets, while smaller window
25

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BRCA
DCFC
L.ADD
BE.CA
0.9
0.69
0.8
0.66
0.65
0.63
0.60
0.6
0.5
CONCH PLIP
UNI
275
CONCH
USI
ST
DONCH PLD
UNI
2TS
CONCH PLIP
UNI
2TSur
Figure 9: Comparisons of C-index (mean + std) with different TME segmentation methods over four cancer datasets.
TOGA-4Z-MAT (BLCA)
tymphooyte
CONCII tymphouple
PLIP:
tymppeoyee
tymphocyle
Iumer
CONCIE: lymphasyte
bughhaetec
PLIP:
Stranto
TODA-A5-A0G1 (UUEC))
braphoyycc
stromt
CONCH:
tymphogte
lympheeyse
PLIP:
PLIP
strama
PLIP
Buioor
Figure 10: Zero-shot TME segmentation comparison of ZTSurv, CONCH, and PLIP.
H.6 Time Complexity Analysis
The complexity of Suru dominated by three components. Firstly, zero-shot TME segmentation on the down-sampled WSI has complexity of O(HI'''d'), Secondly, the complexity of constructing a heterogeneous graph with ní' nodes (n'(dd, + de)). Finally, the graph neural network performs message passing over L layers, with per-layer cost of O'n'k(d, + d.)). In summary, the time complexity of our ZTSurv O(HW'dd') + (n'(d,, + de)) + O(In'k(e k(d, + d,)) = ('''W'd n'(1 + (d, + dz)).
H.7 Limitations and Future Work
In this work, we focus on capturing key TME components, including lymphocyte, tumor, and stroma, for survival prediction. However, as demonstrated in Table including broader range of TME components can potentially lead to better predictive performance. In future work, we will explore more diverse tissue types, such as blood vessels, necrosis, and fibroblasts, to capture more comprehensive representation of the TME and further enhance model robustness.
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Table 5: Parameter analysis of C-index (mean std) over four cancer datasets.
BRCA
UCEC
LUAD
BLCA
Overall
k:
0.633 = 0.037
0.703 0.051
0.636 0.047
0.632 - 0.045
0.651
b =
0.642 + 0.029
0.726 0.113
0.637 0.033
0.637 + 0.042
0.661
0.630 + 0.022
0.698 + 0.063
0.622 + 0.049
0.625 = 0.041
0.644
32
0.635 + 0.022
0.702 + 0.073
0.632 0.052
0.634 + 0.057
0.651
0.642 = 0.029
0.726 0.113
0.637 0.033
0.637 = 0.042
0.661
128
0.635 + 0.037
0.695 0.038
0.631 0.037
0.611 + 0.024
0.643
I Ethical Discussions
Ethical Considerations. The Cancer Genome Atlas (TCGA) dataset used this study a publicly available resource widely utilized in pathology research. Given its open nature and established use, its application in this study does not present significant ethical concerns. The TIL maps employed were generated based publicly available data without the involvement of any identifiable patient information, ensuring no individuals are adversely impacted. As such, this study adheres to ethical guidelines without compromising privacy rights.
Potential Positive Social Impacts. The proposed method has the potential to improve patient outcomes by enabling more accurate and comprehensive analysis of tumor microenvironments. facilitating early diagnosis and personalized treatment planning. Moreover, it can reduce the workload of pathologists by automating routine analyses, potentially increasing the scalability and efficiency of cancer diagnosis in clinical practice.
Potential Negative Social Impacts. As this work focuses cancer survival prediction, it is important to acknowledge potential social impacts, including but not limited to:
Diagnostic Errors. Like all Al-based methods, this approach is not immune to errors. Incorrect predictions misclassifications could have serious consequences for patient care and treatment decisions. Therefore, these tools should serve as decision aids, complementing but not replacing human medical judgment.
Privacy Concerns. WSI datasets can contain sensitive information, and the leakage of such data may pose significant privacy risks to patients. To mitigate this, our study exclu- sively relies publicly available datasets where personal identifiers are either absent or appropriately protected.
27


Reviews:
[{"review_id": "q6RbAvuKND", "reviewer": "Reviewer_UaL5", "summary": "In pathological whole slide images (WSI), prognosis is significantly associated with tumor micro-environment (TME) components, with lymphocyte, stroma and tumor areas being part of this. Training a model to make use of these is difficult due to high resolution of WSIs as well as the lack of annotation of TME components. Authors propose ZTSurv: A model that makes use of PLIP text embeddings to zero-shot predict stroma and tumor areas in a heavily (50x) downsampled WSI, while being directly supervised only for lymphocyte segmentation. Then, a graph of these different regions is constructed and input to a graph convolutional (GCN) network for survival prediction. Experiments on four cancer cohorts from TCGA illustrates superior C-index compared to competing methods.", "questions": "I've summarized my concerns and recommendations in the \"Weaknesses\" section. I am slightly veering on acceptance due to the strength and applicability of the proposed model. If the rebuttal can answer my concerns, I'd gladly revise my score based on further insights. I thank the authors for their work and time.", "limitations": "Yes, the authors address the limitations of their work in supplementary material.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths\n\n* Superior performance compared to competing methods makes this approach useful for survival prediction. \n* The use of low-resolution input rather than full patch processing leads to greatly reduced memory usage and time taken for inference. \n* Leveraging the use of text-based PLIP encodings allows for solid zero-shot predictions on unannotated stroma and tumor classes. \n* Ablation studies show the effects of each component: text and vision prompts for PLIP, homogeneity of the constructed graph based on node type, use of semantic features and negative edges for nodes. \n\nWeaknesses\n\n* The construction relies heavily on PLIP. This makes it hard to ablate the effect of the dataset used in pretraining. Previous heterogeneous graph methods such as [1] do not make use of PLIP. An experiment where the PLIP embeddings are progressively corrupted with noise would clearly illustrate how important they are in the learning process. I appreciate that it is difficult to measure the effect and the improvement that using PLIP embeddings could have on similar methods such as [1], but without these experiments its hard to quantify the actual improvement. \n* The technical contributions appear to be relatively incremental:\n* The use of VLM embeddings for zero-shot class prediction is common, here's an example with PLIP specifically [2].\n* Building a heterogeneous graph for representing TME components has also been done, although with different classes and formulations [1], as also stated in the paper.  \n* The edge creation based on feature similarity with KNN rather than spatial positioning is novel, but minor. \n* The choice of 50x downsampling appears to be somewhat arbitrary. An ablation study showing how much gain there is (or is not) based on higher/lower downsampling ratios (within hardware limits) would offer further insights. \n\n[1] Wu, Junxian, et al. \"Leveraging tumor heterogeneity: Heterogeneous graph representation learning for cancer survival prediction in whole slide images.\" Advances in Neural Information Processing Systems 37 (2024): 64312-64337.\n\n[2] Guo, Miaotian, et al. \"Multiple prompt fusion for zero-shot lesion detection using vision-language models.\" International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2023.", "was_revised": true, "final_justification": "Based on the points I raised in my comments to the authors' rebuttal, I keep my initial score of 4, which I find to be still appropriate due to the lack of significant technical contribution with the knowledge that the paper targets a specific application (pathology).", "rebuttal": "Thanks for your constructive comment. Here are the responses to Weaknesses (W).\n\n**W1. The construction relies heavily on PLIP.**\n\nWe thank the reviewer for such great comment. In fact, while your mentioned study [1] dose not use the PLIP embeddings, it relies on another foundation model i.e., UNI for patch-level feature extraction. For fair comparison between our method and the study in [1] , we apply the same encoder of VLM to extract features (i.e., PLIP and UNI embeddings), and the final survival prediction results are listed below: \n\n| Method            | Pre-trained VLM | BRCA                  | UCEC                  | LUAD                  | BLCA                  | Overall   |\n| ----------------- | --------------- | --------------------- | --------------------- | --------------------- | --------------------- | --------- |\n| ProtoSurv [1]     | PLIP            | 0.620 $\\pm$ 0.012     | 0.698 $\\pm$ 0.125     | 0.629 $\\pm$ 0.035     | 0.624 $\\pm$ 0.040     | 0.643     |\n| **ZTSurv (ours)** | **PLIP**        | **0.642 $\\pm$ 0.029** | **0.726 $\\pm$ 0.113** | **0.637 $\\pm$ 0.033** | **0.637 $\\pm$ 0.042** | **0.661** |\n| ProtoSurv [1]     | UNI             | 0.625 $\\pm$ 0.009     | 0.705 $\\pm$ 0.131     | 0.638 $\\pm$ 0.026     | 0.629 $\\pm$ 0.043     | 0.649     |\n| **ZTSurv (ours)** | **UNI**         | **0.650 $\\pm$ 0.030** | **0.737 $\\pm$ 0.115** | **0.646 $\\pm$ 0.037** | **0.645 $\\pm$ 0.038** | **0.670** |\n\nAs shown in the table above, for both PLIP and UNI encoders, our ZTSurv consistently outperforms [1] across all datasets (BRCA, UCEC, LUAD, BLCA, and Overall), which demonstrates the effectiveness of our approach for survival prediction of human cancers from WSIs.\n\n[1] Wu, Junxian, et al. \"Leveraging tumor heterogeneity: Heterogeneous graph representation learning for cancer survival prediction in whole slide images.\" Advances in Neural Information Processing Systems 37 (2024): 64312-64337.\n\n**W2. The technical contributions appear to be relatively incremental:**\n\nWe appreciate your feedback and would like to to clarify several key points regarding our contributions at first, and then will address your comment point by point. The main contribution of this study is that we propose a novel end-to-end framework ZTSurv for survival prediction of human cancers that can segment different TME components and capture their spatial interactions for clinical outcome prediction. To the best of our knowledge, this is the first unified learning framework that can simultaneously conduct zero-shot tissue segmentation and survival prediction, which allows us to achieve better segmentation accuracy with low annotation burden and improve survival prediction performance, as these two tasks are highly related and thus can mutually enhance each other when learned together. More importantly, we achieve this on 50x down-sampled WSIs, which can significantly reduce the computational cost and storage burden in comparison with the existing studies that are implemented on high resolution whole-slide pathological images (WSIs). \n\n- **The use of VLM embeddings for zero-shot class prediction is common.**\n\n  While it is true that the VLM embeddings for zero-shot prediction have been explored, they mainly focus on classification or object detection tasks [2] [3], and it is still challenging to transfer the zero-shot learning ability of VLM to the tissue segmentation tasks on histopathological images and fewer studies work on it. Accordingly, we present a novel prototype guided zero-shot tissue segmentation model (Section 3.1 ) by the aid of patch-level PLIP embeddings that can achieve satisfied tissue segmentation results.   \n\n- **Building a heterogeneous graph for representing TME components has been done.**\n\n  We acknowledge that heterogeneous graphs have been used to represent TME components for survival prediction in previous works, such as [1]. However, these methods follow a two-stage learning process where the TME patches should be firstly picked out from the whole-slide pathological images for the heterogeneous graph construction via training a tissue segmentation or patch-level classification model. Then, they rely on the constructed heterogeneous graph to build the clinical outcome prediction model. By contrast, we design an one stage learning paradigm that can simultaneously conduct the heterogeneous graph construction and survival prediction tasks in an end-to-end manner. The experimental results shown in Table 1 of the original manuscript clearly demonstrate the advantage of our method in comparison with the existing studies that treat the tasks of heterogeneous graph building and survival prediction independently.\n\n- **The edge creation is novel, but minor.**\n\n  Although the idea of concatenating the semantic information to the node visual embedding for edge representation is simple, the experimental results shown in Table 4 of the appendix clear show its advantage. Such observations also inspire to design more complicated ways to combine the semantic prompts with the visual representation of nodes for further performance improvement. We will point it out as the future research direction in the conclusion section of the revised manuscript.  \n\n[1] Wu, Junxian, et al. \"Leveraging tumor heterogeneity: Heterogeneous graph representation learning for cancer survival prediction in whole slide images.\" Advances in Neural Information Processing Systems 37 (2024): 64312-64337.\n\n[2] Guo, Miaotian, et al. \"Multiple prompt fusion for zero-shot lesion detection using vision-language models.\" International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2023.\n\n[3] Kim, Sanghyun, et al. \"Locality-Aware Zero-Shot Human-Object Interaction Detection.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n**W3. The choice of 50x downsampling appears to be somewhat arbitrary.**\n\nThanks for this great comment. We chose 50x downsampling because the utilized TIL maps are labeled under such resolution [1], and thus we are unable to test the performance of our method on the TIL maps with higher resolution. By contrast, according to your suggestion, we conduct additional experiments to examine the impact of lower-resolution inputs by further downsampling the TIL map to 100×. The results are shown below:\n\n| Downsampling | BRCA              | UCEC              | LUAD              | BLCA              | Overall |\n| ------------ | ----------------- | ----------------- | ----------------- | ----------------- | ------- |\n| 50x          | 0.642 $\\pm$ 0.029 | 0.726 $\\pm$ 0.113 | 0.637 $\\pm$ 0.033 | 0.637 $\\pm$ 0.042 | 0.661   |\n| 100x         | 0.602 $\\pm$ 0.027 | 0.682 $\\pm$ 0.030 | 0.601 $\\pm$ 0.028 | 0.603 $\\pm$ 0.037 | 0.622   |\n\nAs can be observed from the table above, training on the lower resolution TIL map will lead to the decrease for survival prediction. Such experimental results suggest that excessive downsampling reduces the amount of useful information available for survival prediction.\n\n[1] Saltz, Joel, et al. \"Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images.\" Cell Reports 23.1 (2018): 181-193.e7.\n\n\n\nOnce again, we sincerely thank you for your thoughtful review and constructive feedback. If you have any further questions, we would be glad to respond. If you find our responses satisfactory, we would greatly appreciate your reconsideration of the score."}, {"review_id": "PcDoVe8pQS", "reviewer": "Reviewer_Ectr", "summary": "The authors propose a method to address four key challenges in computational pathology for survival prediction: (1) the high computational cost of patch-based processing for gigapixel Whole-Slide Images (WSIs), (2) the limited capacity of existing methods to comprehensively model the Tumor Micro-Environment (TME) and its spatial organization, (3) the reliance of current GNN-based approaches on a two-step pipeline involving pre-segmented TME components, and (4) the inability of standard GNNs to jointly incorporate visual features and semantic information in node representations. To overcome these limitations, the authors introduce ZTSurv, a framework for cancer survival prediction that simultaneously segments TME components, starting from TIL maps, by leveraging pathology-language foundation models. These models fuse semantic and visual information to build heterogeneous graphs over downsampled (50x) WSIs, reducing computational demands. The method is evaluated across four TCGA cancer cohorts, with supporting ablation studies and survival stratification analyses.", "questions": "1. How does the proposed method compare to recent slide-level foundation models (e.g., TITAN, PRISM) that are explicitly designed to capture global spatial and contextual relationships across whole-slide images for survival prediction? Why are these models not considered in the comparisons or discussed in the related work?\n2. Given that the model operates on 50x downsampled WSIs, how does this resolution affect the segmentation quality of fine-grained structures such as TILs and stromal boundaries, and how does it influence the survival prediction performance? Have the authors conducted any ablation studies or resolution comparisons to quantify this trade-off?\n3. Can the authors justify the choice of labeling graph patches using the dominant tissue class within fixed-size regions, especially in areas with high tissue heterogeneity? How do they account for the potential label noise introduced at tissue boundaries or mixed-type regions?\n4. The graph edge construction relies on Pearson correlation between concatenated visual and semantic embeddings. How do the authors ensure that this metric produces biologically meaningful relationships, given the statistical and functional differences between visual and text features? Have alternative or learned similarity measures been considered?\n5. Can the authors clarify how the baseline models were trained, particularly in terms of input resolution? Were they evaluated using full-resolution WSIs or similarly downsampled inputs (e.g., 50x), and how does this affect the fairness and validity of the performance comparison?", "limitations": "The authors adequately addressed the limitations and potential negative societal impact of their work", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1. The manuscript is clearly written and well-organized. While leveraging segmented regions of interest to enhance task performance is a common approach in the literature, the proposed framework introduces a novel and effective perspective by enabling survival prediction and tissue segmentation to co-evolve and mutually reinforce each other during training. Moreover, the model’s ability to discover new tissue compartments, such as stroma and cancerous regions, without requiring pixel-level supervision holds promising implications for broader applications. \n\nWeaknesses\n1. While the manuscript \"critiques\" patch-based aggregation strategies for their inability to capture the spatial heterogeneity of the tumor microenvironment, it does not acknowledge recent advances in slide-level foundation models, such as TITAN [1], PRISM [2], CHIEF[3]…, that are explicitly designed to overcome these limitations by modeling global spatial and contextual relationships across the whole slide. This omission weakens the positioning of the proposed method with respect to the current state-of-the-art.\n[1] Ding, Tong, et al. \"Multimodal whole slide foundation model for pathology.\" arXiv preprint arXiv:2411.19666 (2024).\n[2] Shaikovski, George, et al. \"Prism: A multi-modal generative foundation model for slide-level histopathology.\" arXiv preprint arXiv:2405.10254 (2024).\n[3] Wang, Xiyue, et al. \"A pathology foundation model for cancer diagnosis and prognosis prediction.\" Nature 634.8035 (2024): 970-978.\n2. Even though the proposed method outperforms state-of-the-art approaches (which are presumably trained at original WSI resolution), the authors report reduced computational cost by operating on 50x downsampled WSIs, this design choice raises concerns about the loss of fine-grained information, particularly for tasks such as TILs or stromal boundary segmentation and survival analysis, which rely on high-resolution (0.25-0.5mpp) histological detail. The manuscript lacks an ablation or comparative study to assess the impact of resolution on segmentation accuracy and survival prediction performance. Moreover, no quantitative analysis of runtime or efficiency is provided to justify the proposed trade-off.\n3. The graph is constructed by dividing downsampled WSIs into fixed-size patches and assigning a tissue label based on the dominant class within each patch. While computationally efficient, this heuristic may oversimplify the true spatial heterogeneity of the tumor microenvironment. In regions where multiple tissue types (e.g., tumor-stroma or tumor-lymphocyte interfaces) are closely mixed, assigning a single label to the entire patch risks introducing significant label noise. This can lead to ambiguous or misleading node representations, particularly at boundaries where biologically meaningful transitions occur. Such simplification may undermine the expressiveness of the graph, especially given that downstream survival prediction depends on accurate modeling of fine-grained TME interactions. Without explicit modeling of sub-patch level composition or uncertainty, the proposed node construction method may fail to capture critical spatial cues that are central to both segmentation fidelity and survival risk modeling.\n4. The edge definitions in the graph are based on Pearson correlation computed over concatenated visual and text embeddings from the PLIP encoder. While this fusion aims to create a unified node representation, it merges two fundamentally distinct modalities, visual features (e.g., morphology, texture) and semantic cues (e.g., tissue type, biological role), which differ in both statistical properties and functional relevance. Pearson correlation assumes a linear relationship and equal scaling across dimensions, an assumption that may not hold in this multimodal context. As a result, nodes that are semantically dissimilar but visually similar (or vice versa) may be incorrectly assigned strong connections, potentially introducing biologically implausible edges. Although the authors apply random edge and feature dropout as a generic regularization mechanism to mitigate noisy or spurious correlations, this does not address the underlying limitation of using a fixed, hand-crafted similarity metric that lacks biological interpretability. A more principled solution would involve decoupling the visual and semantic similarity pathways or learning edge weights in a data-driven manner, e.g., through attention-based mechanisms, to better reflect meaningful relationships in the tumor microenvironment.\n\nMinor Weakness: typo at line 283 “furthrt”", "was_revised": true, "final_justification": "After reading the authors’ responses, I find that three of the five main weaknesses I previously identified have been satisfactorily addressed: (W1-2-5) are now supported by new experiments.\n\nTwo points of improvement remain:\n\n- (W3) While the authors acknowledge the limitation raised and plan to include it in the revised manuscript, the concern remains valid. Assigning a dominant tissue label to each graph patch, though consistent with prior works [1][2], may be suboptimal in the presence of heterogeneous tissue regions. The suggested trade-off between patch size and computational complexity is noted, but the issue underscores a limitation in the method’s ability to capture fine-grained heterogeneity. This remains a relevant weakness for the current version of the work.\n- (W4) The authors provide an extensive response supported by new experiments to the concern regarding the use of Pearson correlation on concatenated image-text embeddings, and the additional similarity analysis is appreciated. However, while the supplemental learned similarity experiment shows a slight improvement over the fixed Pearson metric, the performance gains are relatively marginal. The authors acknowledge that Pearson correlation may not fully capture non-linear interactions, yet the proposed learnable attention-based metric will be only briefly explored in the current version of the manuscript. The reliance on a fixed similarity function in the main method still constitutes a limitation, particularly given the known complexity of interactions in the tumor microenvironment. Further investigation, that the authors remand to future work, into more expressive or adaptive similarity functions would strengthen the methodology. \n\nI have also read all other reviews and the authors’ responses. Considering that most concerns have been addressed, aside from the two remaining weaknesses highlighted, I would maintain my score as 4 (Borderline accept)", "rebuttal": "Thanks for your constructive comment. Here are the responses to Weaknesses (W) and Questions (Q).\n\n**W1&Q1. How does the proposed method compare to recent slide-level foundation models.**\n\nThanks for this great comment. We agree that recent slide-level foundation models such as TITAN, PRISM, CHIEF are promising advances toward modeling spatial and contextual information across WSIs. However, they typically adopt a two-stage design, where the modeling of TME structures and survival prediction are handled independently that miss the inherent correlation among them. Moreover, they rely heavily on high-resolution patch-based preprocessing, which requires considerable computation and memory for tiling and patch-level feature storage. In contrast, our method simultaneously realize the TME segmentation and survival prediction tasks in an end-to-end manner on the down-sampled WSIs, which enables joint modeling of spatial tissue structures and prognostic outcomes while reducing computational cost.\n\nWe conduct a comparison using the WSI-level embeddings generated by TITAN, PRISM, and CHIEF for survival prediction. As shown below, our approach outperforms these methods in survival prediction across four cancer types. Moreover, according to your suggestions, we will introduce these slide-level foundation models in the related work of the revised manuscript.\n\n|Model|BRCA|UCEC|LUAD|BLCA|Overall|\n|-|-|-|-|-|-|\n|TITAN|0.614±0.025|0.697±0.031|0.613±0.017|0.610±0.028|0.634|\n|PRISM|0.607±0.022|0.690±0.028|0.607±0.023|0.605±0.037|0.627|\n|CHIEF|0.632±0.027|0.694±0.034|0.641±0.029|0.628±0.031|0.641|\n| **ZTSurv (ours)** | **0.642±0.029** | **0.726±0.113**| **0.637±0.033**| **0.637±0.042**| **0.661**|\n\n**W2&Q2. How does the resolution affect the segmentation and survival prediction performance.**\n\nWe fully agree that spatial resolution can influence performance, particularly for tasks that rely on fine-grained histological detail. However, our method is specifically designed to capture the distributional patterns of TME components such as TILs at the whole-slide level, rather than relying on detailed cellular morphology. As demonstrated in [1], the spatial patterns of TILs are closely associated with patients’ survival, highlighting the power of spatial distribution analysis in addressing clinically relevant questions, even without high-resolution morphological detail. \n\nMoreover, as discussed in the response to W3 of reviewer UaL5, we chose 50x downsampling because the utilized TIL maps are labeled under such resolution [1], and thus we are unable to test the performance of our method on the TIL maps with higher resolution. By contrast, according to your suggestion, we conduct additional experiments to examine the impact of lower-resolution inputs by further downsampling the TIL map to 100×. The results are shown below, along with the average inference time per WSI implemented on a single NVIDIA RTX 4090 GPU.\n\nSurvival prediction results:\n\n|Downsampling|Avg. Inference Time per WSI (s)|BRCA|UCEC|LUAD|BLCA|Overall|\n|-|-|-|-|-|-|-|\n|50x|32|0.642±0.029|0.726±0.113|0.637±0.033|0.637±0.042|0.661|\n|100x|15|0.602±0.027|0.682±0.030|0.601±0.028|0.603±0.037|0.622|\n\nSegmentation results:\n\n|Downsampling|BRCA|UCEC|LUAD|BLCA|Overall|\n|-|-|-|-|-|-|\n|50x|0.574±0.023|0.506±0.147|0.595±0.023|0.608±0.055|0.571|\n|100x|0.552±0.024|0.472±0.129|0.562±0.026|0.573±0.048|0.540|\n\nAs shown in above tables, lowering the resolution to 100× leads to a consistent decline in performance across all cohorts. This suggests that excessive downsampling reduces the amount of useful information available for both tissue segmentation and survival prediction.\n\n[1] Saltz, Joel, et al. \"Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images.\" Cell Reports 23.1 (2018): 181-193.e7.\n\n**W3. Potential label noise introduced at tissue boundaries or mixed-type regions.**\n\nWe thank the reviewer for such insightful comment. Actually, the original WSIs are typically scanned at a spatial resolution of 0.25 μm/pixel. After downsampling the images by a factor of 50, the resulting spatial resolution becomes: 0.25 μm/pixel × 50 = 12.5 μm/pixel. This resolution aligns well with the average size of a single cell, which is typically around 10–15 μm in diameter. \n\nAt this scale, each pixel approximately corresponds to the size of a cell with specific type, and thus the scenario where each pixel in the down-sampled WSIs corresponds to a different tissue type is unlikely happen. By contrast, if the downsampling level achieves to 100x (i.e., 25.0 μm/pixel), each pixel in the down-sampled image will have more chance to involve multiple cells with diverse types that will lead to ambiguous or misleading node representations. We believe this is the one of the reasons that the survival prediction results under the downsampling ratio of 50x is significantly better to that under 100x. Please see the result tables shown in the answer to your previous comment (W2 &Q2). \n\n**Q3. Dominant tissue class within fixed-size regions.**\n\nWe thank the reviewer for such great comment. Similar to previous works [1][2], we assign each graph patch a dominant tissue class label based on the most frequent tissue type within the patch, which has been shown effective in prior studies and our methods. We agree with you that such approximation maybe ineffective on tissues with high heterogeneity. A possible solution is to treat the patch with reduced size as node, but it will bring more graph nodes that will lead to higher computational complexity for graph learning. We will add it as the limitation of this study in the revised manuscript. \n\n[1] Wu, Junxian, et al. \"Leveraging tumor heterogeneity: Heterogeneous graph representation learning for cancer survival prediction in whole slide images.\" Advances in Neural Information Processing Systems 37 (2024): 64312-64337.\n\n[2] Shao, Wei, et al. \"Tumor micro-environment interactions guided graph learning for survival analysis of human cancers from whole-slide pathological images.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.\n\n**W4&Q4. Similarity measures for graph edge construction.**\n\nWe appreciate the reviewer’s thoughtful comments. To investigate the concern that Pearson correlation over concatenated visual and text embeddings may create spurious edges, we conduct a controlled similarity analysis across different types of node pair. Specifically, we compute the mean similarity scores for three types of relationships: (1) Image–Image (Same Type): Pairwise similarity between nodes of the same tissue class based on visual features only. (2) Image–Text (Same Type): Similarity between a node's image embedding and its corresponding text embedding (e.g., tumor image vs. “tumor” text). (3) Image–Text (Different Type): Similarity between a node's image embedding and text embeddings from different tissue classes (e.g., tumor image vs. “stroma” text). The results are summarized below:\n\n|Type|Mean Similarity|Std Dev|\n|-|-|-|\n|Image–Image (Same Type)|0.698|0.018|\n|Image–Text (Same Type)|0.341|0.028|\n|Image–Text (Different Type)|0.103|0.043|\n\nFrom the results, we can observe that image and text embeddings of specific tissues are biologically related but not excessively redundant , which suggests that these two modalities provide complementary information. Consequently, computing Pearson correlation over their concatenated representations offers a meaningful and interpretable way to model interactions within the tumor microenvironment.\n\nWe also agree with the reviewer that a fixed similarity metric such as Pearson correlation might not fully capture complex or non-linear relationships. To explore this further, we implement a learnable similarity function inspired by attention mechanisms. Specifically, we project node embeddings into query and key spaces using learnable matrices and compute edge weights by taking the dot product between queries and keys, followed by a ReLU activation. Here are the results:\n\n|Method|BRCA|UCEC|LUAD|BLCA|Overall|\n|-|-|-|-|-|-|\n|Learned similarity|0.647±0.030|0.734±0.117|0.642±0.035|0.644±0.038|0.667|\n|Pearson similarity|0.642±0.029|0.726±0.113|0.637±0.033|0.637±0.042|0.661|\n\nAs shown in the table, the learned similarity approach achieves improved performance to fixed Pearson correlation. As part of our future work, we plan to conduct more detailed analyses and further explore the potential of data-driven approaches in this context.\n\n**Q5. How the baseline models were trained.**\n\nAll baseline models were trained on full-resolution WSIs, following their original implementations. These methods rely on patch-level inputs extracted at high magnification and cannot be directly applied to down-sampled whole-slide representations. As a result, preprocessing steps such as tiling and patch-level feature extraction are required to prepare the data for these methods. \n\nTo further evaluate performance under a consistent input setting, we additionally compare our method with ProtoSurv [1] on 50× down-sampled WSIs. The results are shown below:\n\n|   |BRCA|UCEC|LUAD|BLCA|Overall|\n|-|-|-|-|-|-|\n|ProtoSurv|0.596±0.035|0.559±0.119|0.563±0.048|0.572±0.038|0.573|\n|ZTSurv (ours)|0.642±0.029|0.726±0.113|0.637±0.033|0.637±0.042|0.661|\n\nThese results demonstrate that our method maintains strong performance at significantly lower resolution, highlighting its efficiency and scalability.\n\n[1] Wu, Junxian, et al. \"Leveraging tumor heterogeneity: Heterogeneous graph representation learning for cancer survival prediction in whole slide images.\" Advances in Neural Information Processing Systems 37 (2024): 64312-64337.\n\nOnce again, we sincerely thank you for your thoughtful review and constructive feedback. If you have any further questions, we would be glad to respond. If you find our responses satisfactory, we would greatly appreciate your reconsideration of the score."}, {"review_id": "sNQCpV9gNj", "reviewer": "Reviewer_FFJs", "summary": "This paper proposes ZTSurv, an end-to-end framework for cancer survival prediction using zero-shot segmentation of Tumor Microenvironment (TME) components on low-resolution Whole Slide Images (WSIs). The method leverages Tumor Infiltrating Lymphocyte (TIL) maps to enable annotation-free segmentation of tumor and stroma via a pathology-language foundation model (PLIP). A heterogeneous graph incorporating visual and semantic features then models spatial interactions for survival prediction. Experiments on four TCGA cancer cohorts show superior performance and reduced computational costs compared to state-of-the-art methods.", "questions": "1. ZTSurv does not leverage GPT-4-generated class descriptions for node representation—relying instead on basic class names. Why not reuse class descriptions(used in Zero-shot Tumor Microenvironment Segmentation stage) to unify semantic alignment across modules? \n2. The paper allows the survival prediction loss to guide the TME segmentation training in reverse (Formula 12: Total loss = Segmentation loss + $\\gamma$ × Survival loss), which may lead to:\n\n(1)\tThe model could mislabel tissues (e.g., classify tumor regions as stroma) if doing so improves survival prediction scores.\n\n(2)\tThe paper doesn’t check if survival guidance introduces segmentation errors. Survival loss backpropagation to segmentation lacks verification.\n\n3. How are the weight coefficients $\\alpha$, $\\beta$ and $\\gamma$ in the loss function determined?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1.\tReduces annotation burden by enabling zero-shot TME segmentation without pixel-level pathologist annotation.\n\n2.\tExceptional efficiency with 3,000× lower memory and 6× faster inference via 50× down-sampled WSIs compared to patch-based methods.\n\n3.\tExtensive validation across four cancer types with multiple metrics (C-index, mIoU, Kaplan-Meier) demonstrates robustness.\n\nWeaknesses:\n1. ZTSurv enables zero-shot segmentation on other two important TME components (i.e., tumor and stroma) but relies on TIL maps requiring manual labeling, transferring annotation burden upstream.\n2. While the paper proposes zero-shot segmentation for tumor and stroma, they only validates lymphocyte predictions via mIoU in Table 2, lacking ground-truth validation of tumor and stroma and influencing clinical trustworthiness. \n3. ZTSurv's tissue ablation study in Table 3 excludes necrosis—a validated independent survival predictor. This contrasts with ProtoSurv's[1] inclusion of necrosis in its 5-class framework in Table 4, where comprehensive ablation proved necrosis essential for optimal prognosis modeling. \n4. Graph construction based on KNN only relies on feature similarity and ignores spatial distance. Verify whether the current method is superior to the traditional spatial graph methods.\n5. The evaluation of mIoU is only performed on lymphocytes with ground truth. For tumor and stroma, there are only a few selected visualization results (Figure 3), which are far from convincing.\n\n[1] Leveraging Tumor Heterogeneity: Heterogeneous Graph Representation Learning for Cancer Survival Prediction in Whole Slide Images", "was_revised": true, "final_justification": "4: Borderline accept", "rebuttal": "Thanks for your constructive comment. Here are the responses to Weaknesses (W) and Questions (Q).\n\n**W1. ZTSurv transfers annotation burden upstream.**\n\nPrevious methods require training a segmentation or classification model that includes all target classes, necessitating manual annotations for each of these categories. In contrast, our approach only requires annotation of the TIL map, significantly reducing the annotation burden. \n\n**W2 & W5. Zero-shot segmentation lacks ground-truth validation of tumor and stroma.**\n\nWe thank the reviewer for such great comment. We would like to clarify that currently, there is no complete semantic segmentation dataset available for TCGA, and the only existing annotation is the TIL map. As a result, quantitative evaluation is limited to the seen class (lymphocyte), for which we report mIoU. Since ground truth annotations for unseen classes (tumor and stroma) are unavailable, direct evaluation of these classes is not feasible. To address this limitation and assess the reliability of our segmentation results, we leverage the foundation model PLIP to compute the image–text alignment between segmented image patches and class-specific text prompts. Specifically, we evaluate the similarity between: (1) tumor patches and the text prompt “tumor”, (2) tumor patches and the text prompt “stroma”, (3) stroma patches and the text prompt“tumor”, and (4) stroma patches and the text prompt“stroma”. We compute the average scores for each of these pairs across four datasets. The results are presented in the table below.\n\n| Patch-Text                   | BRCA | UCEC | LUAD | BLCA |\n| ---------------------------- | ---- | ---- | ---- | ---- |\n| Tumor (Image)-Tumor (Text)   | 0.85 | 0.88 | 0.83 | 0.86 |\n| Tumor( Image)-Stroma (Text)  | 0.31 | 0.33 | 0.29 | 0.35 |\n| Stroma (Image)-Tumor (Text)  | 0.28 | 0.30 | 0.27 | 0.33 |\n| Stroma (Image)-Stroma (Text) | 0.82 | 0.80 | 0.79 | 0.81 |\n\nFrom the results, we observe that the similarity between image and text embeddings of the same tissue type is consistently higher than that between different tissue types, which to some extent reflects the accuracy of our segmentation for unseen classes (tumor, stroma). \n\n**W3. ZTSurv's tissue ablation study in Table 3 excludes necrosis.**\n\nWe thank the reviewer for such great comment. We conduct an additional experiment incorporating necrosis, and here are the results:\n\n|                                        | BRCA              | UCEC              | LUAD              | BLCA              | Overall |\n| -------------------------------------- | ----------------- | ----------------- | ----------------- | ----------------- | ------- |\n| lymphocyte + tumor + stroma            | 0.642 $\\pm$ 0.029 | 0.726 $\\pm$ 0.113 | 0.637 $\\pm$ 0.033 | 0.637 $\\pm$ 0.042 | 0.661   |\n| lymphocyte + tumor + stroma + necrosis | 0.651 $\\pm$ 0.031 | 0.739 $\\pm$ 0.117 | 0.647 $\\pm$ 0.025 | 0.649 $\\pm$ 0.045 | 0.672   |\n\nFrom the experimental results, it is evident that including necrosis indeed improves survival prediction, which aligns with the findings in [1]. We acknowledge this limitation in the Appendix H.7, where we note that including a broader range of TME components, such as blood vessels, necrosis, and fibroblasts, could further enhance predictive performance. In future work, we will explore these additional tissue types to capture a more comprehensive representation of the TME and improve the robustness of our model.\n\n[1] Wu, Junxian, et al. \"Leveraging tumor heterogeneity: Heterogeneous graph representation learning for cancer survival prediction in whole slide images.\" Advances in Neural Information Processing Systems 37 (2024): 64312-64337.\n\n**W4. Graph construction based on KNN only relies on feature similarity and ignores spatial distance.**\n\nWe apologize for the confusion. To clarify, our graph construction method based on KNN incorporates spatial distance (L2 norm). We will correct this in the revised version. Furthermore, to address this concern more directly, we conduct an additional experiment where the graph is constructed based on feature similarity. Here are the results:\n\n|                    | BRCA              | UCEC              | LUAD              | BLCA              | Overall |\n| ------------------ | ----------------- | ----------------- | ----------------- | ----------------- | ------- |\n| feature similarity | 0.639 $\\pm$ 0.031 | 0.726 $\\pm$ 0.097 | 0.635 $\\pm$ 0.028 | 0.638 $\\pm$ 0.036 | 0.660   |\n| spatial distance   | 0.642 $\\pm$ 0.029 | 0.726 $\\pm$ 0.113 | 0.637 $\\pm$ 0.033 | 0.637 $\\pm$ 0.042 | 0.661   |\n\n**Q1. ZTSurv does not leverage GPT-4-generated class descriptions for node representation.**\n\nWe appreciate this valuable suggestion. We conduct a comparison experiment where we incorporate GPT-4-generated class descriptions for node representation. Here are the results:\n\n| Node Representation | BRCA              | UCEC              | LUAD              | BLCA              | Overall |\n| ------------------- | ----------------- | ----------------- | ----------------- | ----------------- | ------- |\n| class name          | 0.642 $\\pm$ 0.029 | 0.726 $\\pm$ 0.113 | 0.637 $\\pm$ 0.033 | 0.637 $\\pm$ 0.042 | 0.661   |\n| class description   | 0.651 $\\pm$ 0.026 | 0.737 $\\pm$ 0.120 | 0.644 $\\pm$ 0.028 | 0.645 $\\pm$ 0.039 | 0.669   |\n\nFrom the results, incorporating GPT-4-generated class descriptions for node representation contributes to improved performance on multiple datasets. We again appreciate your insightful suggestion.\n\n**Q2. Survival loss backpropagation to segmentation lacks verification.  & Q3. How are the weight coefficients in the loss function determined.**\n\nWe thank the reviewer for raising these important questions. Recent studies have demonstrated that accurate modeling of the TME is critical for reliable survival prediction [1] [2] [3]. This suggests that segmentation and survival prediction are inherently aligned objectives in the context of histopathology analysis. To verify the effect of survival-guided segmentation, we compare the performance of zero-shot segmentation with and without survival guidance in Table 2 of the original manuscript. The results indicate that incorporating survival supervision leads to improved segmentation accuracy, supporting the effectiveness of this joint optimization strategy. \n\nHowever, we agree with the reviewer that the balance between segmentation and survival loss is crucial. In our formulation, this trade-off is controlled by the hyperparameter $\\gamma$. To examine its effect, we conduct experiments under different $\\gamma$ values and report both survival prediction and segmentation performance across four datasets.\n\nSegmentation results under different $\\gamma$ values:\n\n| $\\gamma$ | BRCA              | UCEC              | LUAD              | BLCA              | Overall |\n| -------- | ----------------- | ----------------- | ----------------- | ----------------- | ------- |\n| 5        | 0.556 $\\pm$ 0.025 | 0.482 $\\pm$ 0.034 | 0.581 $\\pm$ 0.019 | 0.590 $\\pm$ 0.026 | 0.552   |\n| 10       | 0.561 $\\pm$ 0.028 | 0.500 $\\pm$ 0.131 | 0.589 $\\pm$ 0.021 | 0.595 $\\pm$ 0.028 | 0.561   |\n| 20       | 0.574 $\\pm$ 0.023 | 0.506 $\\pm$ 0.147 | 0.595 $\\pm$ 0.023 | 0.608 $\\pm$ 0.055 | 0.571   |\n| 50       | 0.546 $\\pm$ 0.026 | 0.475 $\\pm$ 0.116 | 0.570 $\\pm$ 0.020 | 0.582 $\\pm$ 0.049 | 0.543   |\n\nSurvival prediction results under different $\\gamma$ values:\n\n| $\\gamma$ | BRCA              | UCEC              | LUAD              | BLCA              | Overall |\n| -------- | ----------------- | ----------------- | ----------------- | ----------------- | ------- |\n| 5        | 0.635 $\\pm$ 0.030 | 0.713 $\\pm$ 0.118 | 0.624 $\\pm$ 0.036 | 0.629 $\\pm$ 0.040 | 0.650   |\n| 10       | 0.641 $\\pm$ 0.028 | 0.723 $\\pm$ 0.112 | 0.632 $\\pm$ 0.039 | 0.635 $\\pm$ 0.038 | 0.658   |\n| 20       | 0.642 $\\pm$ 0.029 | 0.726 $\\pm$ 0.113 | 0.637 $\\pm$ 0.033 | 0.637 $\\pm$ 0.042 | 0.661   |\n| 50       | 0.631 $\\pm$ 0.031 | 0.710 $\\pm$ 0.114 | 0.624 $\\pm$ 0.037 | 0.627 $\\pm$ 0.041 | 0.648   |\n\nFrom these results, we observe that when $\\gamma$ is set to a very small value or to 0, as in the original Table 2 where no survival guidance is applied, segmentation lacks prognostic supervision and tends to capture less clinically meaningful structures, leading to reduced segmentation accuracy. In contrast, when $\\gamma$ is too large (for example, $\\gamma$=50), the survival loss dominates the training objective. This can mislead the segmentation module, as it begins to overfit to survival-related global signals at the expense of precise spatial delineation.\n\nFor the other coefficients in the loss function i.e., $\\alpha$ and $\\beta$ are set following the configurations from previous work [4].\n\n[1] Wu, Junxian, et al. \"Leveraging tumor heterogeneity: Heterogeneous graph representation learning for cancer survival prediction in whole slide images.\" Advances in Neural Information Processing Systems 37 (2024): 64312-64337.\n\n[2] Shao, Wei, et al. \"Tumor micro-environment interactions guided graph learning for survival analysis of human cancers from whole-slide pathological images.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.\n\n[3] Han, Minghao, et al. \"Multi-scale heterogeneity-aware hypergraph representation for histopathology whole slide images.\" 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024.\n\n[4] Zhou, Ziqin, et al. \"Zegclip: Towards adapting clip for zero-shot semantic segmentation.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023.\n\n\n\nOnce again, we sincerely thank you for your thoughtful review and constructive feedback. If you have any further questions, we would be glad to respond. If you find our responses satisfactory, we would greatly appreciate your reconsideration of the score."}, {"review_id": "j0aHQo15vK", "reviewer": "Reviewer_WTnk", "summary": "This paper addresses a topic of significant theoretical importance and clinical value. Confronting the core challenges of current cancer survival analysis based on whole-slide pathology images (WSIs), namely high computational costs, strong dependency on annotations, and the separation of segmentation and prediction tasks, the authors propose a novel end-to-end framework named ZTSurv. This framework integrates zero-shot tumor microenvironment (TME) segmentation with heterogeneous graph learning, achieving efficient and accurate survival prediction on low-resolution WSIs.", "questions": "The method's starting point is a pre-existing TIL map. How would the performance of the zero-shot segmentation and final survival prediction be affected if the initial TIL map is of low quality or contains noise? How robust is the model to this?\n(This is just a small personal question, if it is not convenient, the author does not have to specifically add the experimental response, just need to be reflected in future work)", "limitations": "**Weaknesses:**\n1. The paper's core contribution includes \"zero-shot TME segmentation.\" According to the paper's definition, this term is used because the model's loss is calculated only on the \"lymphocyte\" class, with no supervision for \"tumor\" and \"stroma\". However, in academic circles, a strict definition of \"Zero-shot Learning\" typically implies that the model has not seen any samples of the target classes during training. The training WSIs contain all three tissue types simultaneously. The model has visually \"seen\" tumor and stroma, even if it didn't receive label signals for them. The model receives explicit prior knowledge about the target classes through the LLM-generated text descriptions (e.g., \"This is the tumor in the pathology image, typically appearing as...\"). This setup is closer to \"Zero-label Learning\" or a specific form of \"Weakly Supervised Learning.\" While this does not diminish the method's novelty, using the term \"Zero-shot\" may be debatable to some reviewers. Suggestion: The authors could either more precisely define their use of \"zero-shot\" in the context of their specific task or add a brief discussion clarifying the similarities and differences with traditional zero-shot learning to avoid ambiguity and make the argument more robust.\n2. In the NeurIPS checklist in the appendix (Question 16, \"Declaration of LLM usage\"), the authors' answer is \"[NA],\" with the justification that \"The core method development in our study does not involve LLMs as any important, original, or non-standard components.\". However, in the main body of the paper (Sec 3.1, Sec 4.2) and Appendix D, the authors explicitly state that they used GPT-4 as the LLM to generate detailed text descriptions for different tissue categories. These text descriptions are a crucial part of generating the \"prototypes\" that guide the zero-shot segmentation and are undoubtedly an important component of the core methodology. These two statements are in direct contradiction.\n3. The paper effectively proves that the components of the heterogeneous graph are beneficial for performance through ablation studies. However, it lacks a deeper insight into why they are effective. The authors propose an excellent hypothesis in the introduction: \"we have more chance to see the TME component of lymphocyte near the tumor region for long survival patients\". Yet, there is no experiment designed to verify if ZTSurv actually learns this pattern.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n1. This paper performs analysis directly on 50x down-sampled low-resolution WSIs, dramatically reducing memory and time costs. This is a massive breakthrough for the scalability and practicality of large-scale clinical data analysis.\n2. One of the paper's main highlights is its zero-shot TME segmentation strategy. By using only existing tumor-infiltrating lymphocyte maps as supervision and leveraging a pathology-language foundation model with class descriptions generated by a large language model, it achieves unsupervised segmentation of tumor and stroma regions.\n3. When constructing the heterogeneous graph, the authors not only used visual features but also innovatively incorporated semantic features to represent the nodes of different TME components. This dual \"visual + semantic\" representation enhances the model's ability to distinguish between TME components and captures their complex interactions through edges with continuous attributes, making the graph construction more refined and biologically meaningful.\n4. The paper was validated on four major cancer types from TCGA (BRCA, UCEC, LUAD, BLCA), which is representative. It used industry-recognized evaluation metrics such as the C-index, Kaplan-Meier survival curves, and mIoU, ensuring the reliability of the results.", "was_revised": true, "final_justification": "The authors have addressed most of my concerns. I will raise my score to 5.", "rebuttal": "Thanks for your constructive comment. Here are the responses to Weaknesses (W) and Questions (Q).\n\n**W1. Definition of zero-shot learning.**\n\nWe apologize for the misunderstanding caused. In fact, our method follows a transductive zero-shot learning approach. As a matter of fact, the generalized zero-shot learning can be divided into two settings: inductive and transductive. In the inductive setting, only information from seen classes is used during training. In contrast, the transductive setting allows access to partial information (e.g., class names or visual features) from unseen classes except for the ground truth labels in the training process. We will clarify this distinction in the revised version. Thank you for your valuable suggestion.\n\n**W2. Declaration of LLM usage.**\n\nWe apologize for the inconsistency between our response in the NeurIPS checklist and the main body of the paper. We recognize that the use of GPT-4 to generate detailed text descriptions for different tissue categories is crucial in our method. We will update the checklist in the revised version to accurately reflect the role of LLMs in our approach. Thank you for pointing this out.\n\n**W3. There is no experiment designed to verify if ZTSurv actually learns the pattern.**\n\nWe thank the reviewer for such insightful comments. Due to the limitation of uploading the visualization results in the rebuttal phase, we instead follow the study in [1], which calculates the mean edge weights between tumor and lymphocyte nodes in the constructed graph for long-survival and short-survival patients based on the patient stratification results in Fig.2. Intuitively, the weights of the edges that connect tumor and lymphocyte should be higher for patients in low survival risk group, since the strong interactions between lymphocyte and tumor regions will indicate a brisk immune response to human cancers [2], and the experimental results denoted below table is also consistent with the above immune knowledge that can demonstrate the effectiveness of our method.\n\n| Patients       | BRCA | UCEC | LUAD | BLCA |\n| -------------- | ---- | ---- | ---- | ---- |\n| Long Survival  | 0.77 | 0.69 | 0.70 | 0.72 |\n| Short Survival | 0.35 | 0.38 | 0.23 | 0.29 |\n\n[1] Zuo, Yingli, et al. \"Identify consistent imaging genomic biomarkers for characterizing the survival-associated interactions between tumor-infiltrating lymphocytes and tumors.\" International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2022.\n\n[2] Saltz, Joel, et al. \"Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images.\" Cell reports 23.1 (2018): 181-193.\n\n**Q1. How would the performance be affected if the initial TIL map is of low quality or contains noise.**\n\nThanks for this great comment. To assess the robustness of our model to label noise in TIL maps, we randomly perturb 5% and 10% of the labels in the original TIL map and evaluate their performance for both segmentation and survival prediction tasks.\n\nSegmentation results under different label noise ratio:\n\n| Noise Level | BRCA              | UCEC              | LUAD              | BLCA              | Overall |\n| ----------- | ----------------- | ----------------- | ----------------- | ----------------- | ------- |\n| 0%          | 0.574 $\\pm$ 0.023 | 0.506 $\\pm$ 0.147 | 0.595 $\\pm$ 0.023 | 0.608 $\\pm$ 0.055 | 0.571   |\n| 5%          | 0.570 $\\pm$ 0.027 | 0.494 $\\pm$ 0.138 | 0.593 $\\pm$ 0.025 | 0.602 $\\pm$ 0.054 | 0.565   |\n| 10%         | 0.561 $\\pm$ 0.032 | 0.485 $\\pm$ 0.142 | 0.585 $\\pm$ 0.029 | 0.595 $\\pm$ 0.058 | 0.557   |\n\nSurvival prediction results under different label noise ratio:\n\n| Noise Level | BRCA              | UCEC              | LUAD              | BLCA              | Overall |\n| ----------- | ----------------- | ----------------- | ----------------- | ----------------- | ------- |\n| 0%          | 0.642 $\\pm$ 0.029 | 0.726 $\\pm$ 0.113 | 0.637 $\\pm$ 0.033 | 0.637 $\\pm$ 0.042 | 0.661   |\n| 5%          | 0.639 $\\pm$ 0.031 | 0.721 $\\pm$ 0.108 | 0.633 $\\pm$ 0.040 | 0.636 $\\pm$ 0.049 | 0.657   |\n| 10%         | 0.631 $\\pm$ 0.035 | 0.714 $\\pm$ 0.105 | 0.625 $\\pm$ 0.046 | 0.627 $\\pm$ 0.051 | 0.649   |\n\nAs can be seen from the tables above, although the prediction results for both segmentation and survival prediction tasks are slightly decreased when the label noises are introduced, our method is general robust to the TIL maps with low-level label noise, showing its potential in real-world clinical applications, where perfectly labeled data is often limited.\n\n\n\nOnce again, we sincerely thank you for your thoughtful review and constructive feedback. If you have any further questions, we would be glad to respond. If you find our responses satisfactory, we would greatly appreciate your reconsideration of the score."}]

Author rebuttal:

Meta-review: This paper presents a method for cancer survival analysis that leverages zero-shot TME segmentation on low-resolution WSIs. The work is motivated by the need to reduce computational costs while maintaining prediction performance. Reviewers find the problem significant and the proposed framework creative, with the approach showing promising results for both segmentation and downstream survival prediction. While there are some concerns regarding the extent of experimental validation and the limited technical novelty, the rebuttal provides clarifications and additional results that address part of these issues. Considering that the paper tackles an important problem in computational pathology and makes a valuable contribution, it is recommended for acceptance, with the suggestion that the authors incorporate the reviewers’ feedback to further strengthen the final version.
Decision: Accept (poster)