Unleashing the Potential of Vision-Language Pre-Training for 3D Zero-Shot Lesion Segmentation via Mask-Attribute Alignment

## Abstract

Abstract Recent advancements in medical vision-language pre-training models have driven significant progress in zero-shot disease recognition. However, transferring image-level knowledge to pixel-level tasks, such as lesion segmentation in 3D CT scans, remains a critical challenge. Due to the complexity and variability of pathological visual characteristics, existing methods struggle to align fine-grained lesion features not encountered during training with disease-related textual representations. In this paper, we present Malenia , a novel m ulti-sc a le le sio n -level mask-attr i bute a lignment framework, specifically designed for 3D zero-shot lesion segmentation. Malenia improves the compatibility between mask representations and their associated elemental attributes, explicitly linking the visual features of unseen lesions with the extensible knowledge learned from previously seen ones. Furthermore, we design a Cross-Modal Knowledge Injection module to enhance both visual and textual features with mutually beneficial information, effectively guiding the generation of segmentation results.
Comprehensive experiments across three datasets and 12 lesion categories validate the superior performance of Malenia . Codes will be publicly available.

## 1 Introduction

3D medical image segmentation has witnessed rapid advancements in recent years  (Isensee et al., ; Tang et al., ; Ye et al., ; Liu et al., ; Chen et al., ; Zhang et al., ).
However, most tate-of-the-art (SOTA) methods are restricted to a closed-set setting, where they can only predict categories present in the training dataset and typically fail to generalize to unseen disease categories.
Given the diversity and prevalence of new anomalies in clinical scenarios, along with the challenges of medical data collection, there is an increasing demand for zero-shot models capable of handling unseen diseases in an open-set setting.

The advent of vision-language pre-training methods, particularly CLIP (Radford et al., ), has illuminated a new paradigm for remarkable zero-shot object recognition. This breakthrough also paves the way for significant advancements in zero-shot disease detection and diagnosis. Numerous recent methods (Huang et al., ; Tiu et al., ; Wu et al., ; Phan et al., ; Hamamci et al., ) align visual and textual features of paired medical image-report data, enabling transferable cross-modal representations. However, leveraging the zero-shot capability of vision-language pre-training for 3D lesion/tumor segmentation remains a scarcely explored area. This extension is nontrivial and faces two obvious challenges: (i) The substantial gap between the upstream contrastive pre-training task and the downstream per-pixel dense prediction task. The former focuses on aligning image-level global representations with text embeddings, while the latter requires fine-grained lesion-level visual understanding. This inherent gap necessitates the development of more advanced fine-grained vision-language alignment techniques that can facilitate the perception of nuanced, patient-specific pathological visual clues based on the text descriptions. (ii) Lesions can exhibit significant variations in shape and size, and may present with blurred boundaries. Models often struggle when encountering unseen lesion types due to their out-of-distribution visual characteristics. Simply using text inputs, such as raw reports (Boecking et al., ; Tiu et al., ; Hamamci et al., ), or relying on common knowledge of disease definitions (Wu et al., ; Jiang et al., ), is insufficient for learning generalized representations needed to segment novel lesions not mentioned in the training dataset.

Motivated by the aforementioned limitations, we introduce Malenia, a novel multi-scale lesion-level mask-attribute alignment framework for superior zero-shot lesion segmentation. Malenia first leverages multi-scale mask representations with inherent boundary information to capture diverse lesion regions, then matches fine-grained visual features of lesions with text embeddings, effectively bridging the gap between the contrastive pre-training task and the per-pixel dense prediction task.
To learn extensible representations that are robust to the out-of-distribution visual characteristics of unseen lesions, we incorporate domain knowledge from human experts to structure textual reports into descriptions of various elemental disease visual attributes, such as shape, location, density, and specific patterns related to disease manifestations. Despite the significant variability among lesions, these fundamental attributes are shared across different diseases and are often represented similarly in images. By aligning mask representations of lesions with their corresponding visual attributes, the model mimics the decision-making process of human experts, explicitly linking the visual features of unseen diseases to the intrinsic attributes learned from seen lesions. This alignment significantly enhances the model’s ability to recognize and segment various unseen lesions.
Furthermore, we propose a novel Cross-Modal Knowledge Injection (CMKI) module in Malenia, inspired by the observation that visual and textual embeddings, after feature alignment, are complementary and can mutually reinforce each other. Unlike existing language-driven segmentation methods (Liu et al., ; Jiang et al., ), which keep text embeddings fixed, the CMKI module updates both mask and attribute embeddings to facilitate fine-grained multi-modal feature fusion. We leverage both enhanced mask and attribute embeddings to generate predictions by matching query features with image features, and then ensemble these predictions to produce the final segmentation results, demonstrating improved performance for both seen and unseen lesions. To thoroughly validate the effectiveness of Malenia, we evaluate its segmentation performance on both seen and unseen lesions using the MSD (Antonelli et al., ), KiTS23 (Heller et al., ), and a curated real-world in-house dataset. Malenia consistently outperforms state-of-the-art methods across 12 lesion categories.
Our contributions can be summarized as follows:

- •
We present Malenia, a novel multi-scale lesion-level mask-attribute alignment framework that captures extensible multi-modal representations for significantly improved zero-shot lesion segmentation by effectively matching the fine-grained visual appearances of new diseases with the textual representations of various fundamental pathological visual attributes.
- •
Malenia introduces a novel Cross-Modal Knowledge Injection (CMKI) module that enriches both mask and text embeddings with mutually beneficial information through feature fusion, leveraging their complementary strengths to further enhance lesion segmentation performance.
- •
State-of-the-art lesion segmentation performance in the zero-shot setting across three datasets. Malenia significantly outperforms previous methods, and key ablation experiments demonstrate the effectiveness of our strategies in handling lesions with varying characteristics.

## 2 Related Works

Medical Vision-Language Pre-training.
Numerous methods (Huang et al., ; Tiu et al., ; Wu et al., ; Phan et al., ; Hamamci et al., ; Lai et al., )
build upon CLIP and align medical images with their corresponding reports or disease definitions to enable zero-shot disease classification. However, these works focus on single body parts (e.g., chest), which limits their applicability in broader medical contexts. Additionally, these methods primarily adhere to a paradigm that matches text embeddings with global image-level or patch-level semantics, originally designed for classification tasks. When adapting their zero-shot capability to fine-grained segmentation tasks, mismatches can occur due to the model’s inability to align text embeddings with detailed pixel features. A recent work, CT-GLIP (Lin et al., ) uses organ-level vision-language alignment for zero-shot organ classification and abnormality detection, but due to architectural limitations, it requires fine-tuning with a segmentation head and lacks zero-shot segmentation capabilities. In contrast, we extend the zero-shot capability of vision-language pre-training from image-level to pixel-level by aligning lesion features with fundamental multi-aspect disease visual attributes.

Zero-Shot 3D Medical Image Segmentation.
Motivated by the impressive zero-shot performance of SAM (Kirillov et al., ) and SAM 2 (Ravi et al., ) in natural images, numerous studies have evaluated their application in medical image segmentation (Wald et al., ; Zhang et al., ; Huang et al., ; Yamagishi et al., ). However, due to the significant domain gap, directly applying SAM or SAM 2 to medical images typically results in unsatisfactory performance. Consequently, recent works (Ma et al., ; Guo et al., ; Shen et al., ; Zhu et al., ; Shaharabany & Wolf, ) explore effective adaptations of prompted SAM or SAM 2 on medical datasets. Nevertheless, these SAM adaptations require prompts (points, bounding boxes, or masks) sampled from ground truth during testing, which demands significant expertise and is often impractical in real-world clinical scenarios. As a result, prompt-free SAM adaptation methods (Hu et al., ; Zhang & Liu, ; Cheng et al., ; Aleem et al., ) have also been proposed. To the best of our knowledge, although the current SAM-based 3D medical image segmentation methods demonstrate promising zero-shot performance in segmenting certain organs in CT scans, they have not been evaluated or proven effective when confronted with unseen lesions that have less defined structures. Apart from SAM-based models, a self-prompted method, ZePT (Jiang et al., ), achieves competitive zero-shot tumor segmentation performance by matching class-agnostic mask proposals with text descriptions of general medical knowledge. However, this method does not leverage patient-specific information from reports and overlooks the multi-aspect visual attributes shared across different diseases, leading to compromised zero-shot performance.
Our approach goes a step further, achieving significantly superior zero-shot lesion segmentation performance without the need for complex visual prompts.

## 3 Method

Fig. 1 illustrates the pipeline of Malenia. It is built upon the recent mask-based segmentation method Mask2Former (Cheng et al., ), which has achieved promising performance in the field of medical image segmentation (Yuan et al., ; Chen et al., ; Jiang et al., ). Below, we introduce (i) our novel multi-scale mask-attribute alignment strategy (Sec.3.1) that effectively aligns lesion representations with text embeddings of fundamental disease attributes; (ii) the Cross-Modal Knowledge Injection module (Sec.3.2) for multi-modal feature representation enhancement; and (iii) the overall training objectives and inference details (Sec. 3.3). We provide the preliminaries, discussing Mask2Former and the problem formulation of zero-shot lesion segmentation, as prior information required for our work in Appendix A.

Figure: Figure 1: Overview of the training and inference framework of Malenia. The key contributions of our work are two simple but effective designs: the Multi-scale Fine-Grained Mask-Attribute Alignment and the Cross-Modal Knowledge Injection module. Incorporating these designs empowers our vision-language pre-training framework, resulting in a strong zero-shot segmentation model.
Refer to caption: x1.png

### 3.1 Multi-Scale Mask-Text Contrastive Learning

Given a set of $B$ image-report pairs, ${\mathbb{D}}=\{({\mathcal{I}}_{1},{\mathcal{R}}_{1}),...,({\mathcal{I}}_{B},{
\mathcal{R}}_{B})\}$, we aim to establish lesion-level alignment between the mask and text representations within the multi-modal feature space. We first adopt a 3D image encoder to extract high-level features $f$ from the input 3D images $\displaystyle{\mathcal{I}}$. Then a 3D image decoder gradually upsamples $f$ to generate multi-scale high-resolution per-pixel image features $F^{i}\in\mathbb{R}^{H^{i}\times W^{i}\times D^{i}\times C^{i}}$. Here, $H^{i}$, $W^{i}$, $D^{i}$ and $C^{i}$ denote the height, width, depth and channel dimension of $F^{i}$, respectively, which vary depending on the resolution level.
Subsequently, we feed $N$ learnable mask tokens (queries) $M=\{m_{1},\cdot\cdot\cdot,m_{N}\}$, along with successive feature maps $F^{i}$ from the initial three layers of the 3D image decoder into a Transformer decoder with $3$ blocks in a round-robin fashion to process the mask tokens.
At each block $i\in[1,3]$, the mask tokens $M$ undergoes a series of layer-wise attention refinements, including cross attention and self-attention as follows:

$$ $\centering\displaystyle M^{(i)}_{cross}=\text{CrossAttn}(q^{(i)},k^{(i)},v^{(i )}),\quad M^{(i+1)}=\text{SelfAttn}(M^{(i)}_{cross})\@add@centering$ (1) $$

Here, $q^{(i)}$, $k^{(i)}$, and $v^{(i)}$ denote the query matrices of mask tokens and the key and value matrices of image features from the 3D image decoder at the $i$-th resolution level, respectively.
During the cross-attention phase, mask tokens interact with image features, focusing on the specific regional context within the image. In the self-attention phase, mask tokens interact with each other to enhance the understanding of relationships between different anatomical areas. Building on the segment-level embeddings of mask tokens, we establish fine-grained alignment between mask and text representations through a contrastive learning approach. This vision-language alignment in Malenia has three novel key components:

(1) Utilization of Multi-Scale Features.
Existing methods (Jiang et al., ; Lin et al., ) overlook the advantage of leveraging multi-scale visual features during cross-modal alignment. In contrast, we match the hierarchical mask token embeddings from different Transformer decoder blocks with text features. This approach enables the model to accurately capture and segment across a range of mask sizes. Specifically, the mask tokens interact with image features whose dimensions are set as $(h^{i},w^{i},D^{i})=(H/32,W/32,D/32),(H/16,W/16,D/16),(H/8,W/8,D/8)$ for blcoks $i=1,2,3$, respectively. This variation in feature resolution across blocks ensures mask-text alignment at different scales, which is crucial for segmenting classes with large size variations, such as tumors.

(2) Dissecting Reports into Descriptions of Fundamental Disease Attributes.
Human experts make diagnoses by carefully analyzing key image features that describe distinctive lesion attributes (e.g., shape and density) across different disease classes (Ganeshan et al., ; Nobel et al., ).
Drawing from this inspiration, we consult medical experts and decompose reports into structured descriptions of $8$ shared visual attributes of disease imaging characteristics, including location, shape, density, density variations, surface characteristics, enhancement status, relationship with surrounding organs and specific features.
Specifically, we adopt a semi-automatic pipeline to transform patients’ reports into structured descriptions. First, we prompt the Large Language Model (LLM) GPT-4 (Achiam et al., ) to extract descriptions related to the lesions from the findings section of each report.
Then, two experienced radiologists collaborate to review, correct, supplement, and expand the GPT-generated visual descriptions based on the CT images and corresponding original reports. Leveraging disease attribute descriptions offers two key advantages. First, disease attributes provide fine-grained prior knowledge about the visual manifestations of pathologies, inherently improving alignment with target diseases. Second, new diseases can be described using the elemental visual attributes of previously seen diseases, thereby enhancing the model’s zero-shot capability.

(3) Multi-Positive Contrastive Loss.
Given the multi-scale lesion-level mask embeddings $M$ and visual attribute descriptions, we construct multiple positive and negative pairs, which are then used to learn to optimize a distance metric that brings the positive pairs closer while pushing the negative pairs away.
At the $i$-th resolution scale, we obtain $N$ binary mask proposals $BM^{(i)}\in[0,1]^{H^{i}\times W^{i}\times D^{i}\times N}$ by a multiplication operation between the mask embeddings and image features followed by a Sigmoid. We then apply bipartite matching between the upsampled binary mask proposals and the ground truth masks, following (Cheng et al., ), to select $S$ foreground mask tokens that correspond to the $S$ lesions in the input 3D CT image in a one-to-one manner. Next, we feed the ground truth descriptions of all visual attributes into the text encoder followed by a MLP layer to acquire the text embeddings $T=\{t_{1},\cdots,t_{R}\}$, where $R$ denotes the number of different attribute descriptions. In our pipeline, we also require a background category for the $N-S$ background mask tokens that do not have a matched ground truth segment. Therefore, we add an extra learnable embedding, $t_{0}\in\mathbb{R}^{C}$, representing “no lesion found”.
Finally, each foreground mask token is paired with its corresponding eight distinct text features, forming multiple positive sample pairs.
While each background mask token is paired with $t_{0}$.
For the $j$-th mask token, let ${\mathbb{P}}_{j}=\{k|(m_{j},t_{k})\text{ is a positive pair}\}$ represent the set of all its positive text embedding indices, and let ${\mathbb{N}}_{j}=\{k|(m_{j},t_{k})\text{ is a negative pair}\}$ represent the set of all its negative text embedding indices.
We calculate the pairwise similarity score $S(m_{j},t_{k})$ between the $j$-th mask token $m_{j}$ and the $k$-th text embedding $t_{k}$ as a dot product, normalized by a temperature parameter $\tau$, given by $S(m_{j},t_{k})=\frac{m_{j}\cdot t_{k}}{\tau}$.
The lesion-level mask-attribute alignment at the $i$-th resolution level is refined using a contrastive loss function ${\mathcal{L}}_{sim}^{(i)}$ designed to maximize the similarity scores of positive pairs while minimizing those of negative pairs. Since each lesion has eight positive text embeddings of visual attributes, our framework includes multiple positive pairs for each foreground mask token. The commonly used $N$-pair loss (Sohn, ) and InfoNCE loss (Oord et al., ), which handle a single positive pair and multiple negative pairs, are not suitable. Therefore, we adopt the Multi-Positive NCE (MP-NCE) loss (Lee et al., ) to define the ${\mathcal{L}}_{sim}^{(i)}$ as:

$$ ${\mathcal{L}}_{sim}^{(i)}=-\frac{1}{N}\sum_{j=1}^{N}\mathbb{E}_{k\in{\mathbb{P }}_{j}}\Big{[}\log\frac{\exp\big{(}S(m_{j}^{(i)},t_{k})\big{)}}{\exp\big{(}S(m _{j}^{(i)},t_{k})\big{)}+\sum_{n\in{\mathbb{N}}_{j}}\exp\big{(}S(m_{j}^{(i)},t _{n})\big{)}}\Big{]}$ (2) $$

In this way, we explicitly brings lesion-level mask embeddings closer to their corresponding attribute features while distancing them from unrelated ones. This enables the textual features of each attribute to act as a bridge between the visual features of different diseases, effectively improving the model’s zero-shot performance by linking the attributes of unseen lesions with base visual knowledge. Moreover, we apply ${\mathcal{L}}_{sim}^{(i)}$ to output mask tokens from each Transformer decoder block, utilizing multi-scale feature maps. The multi-scale lesion-level mask-attribute alignment loss is formulated as ${\mathcal{L}}_{sim}=-\frac{1}{L}\sum_{i=1}^{L}{\mathcal{L}}_{sim}^{(i)}$, where $L=3$ denotes the number of Transformer Decoder blocks.

### 3.2 Cross-Modal Knowledge Injection Module

In Malenia, we introduce a novel Cross-Modal Knowledge Injection (CMKI) module to generate the final segmentation predictions. This module enriches the features of one modality by incorporating information from another, enabling deeper understanding and improved feature representations. Specifically, we fuse the output mask tokens $m_{i},i\in[1,N]$ from the last Transformer decoder block with their corresponding positive attribute embeddings. As shown in Fig. 1 (a), for each mask token, we first concatenate all its corresponding attribute embeddings $t_{j},j\in{\mathbb{P}}_{i}$, and transform them into a single text embedding $t_{i}$ using an MLP layer to obtain the textual features for a comprehensive description of the mask token $m_{i}$. Then we accomplish deep fusion between two modalities through a series of cross-attention and self-attention layers:

$$ $\displaystyle\hat{m}_{i}=\text{SelfAttn}\big{(}\text{CrossAttn}(q_{m},k_{t},v_ {t})+m_{i}\big{)},\quad\hat{t}_{i}=\text{SelfAttn}\big{(}\text{CrossAttn}(q_{t },k_{m},v_{m})+t_{i}\big{)}$ (3) $$

Here, $q_{m}$, $k_{m}$, and $v_{m}$ represent the query, key, and value matrices of the mask tokens, while $q_{t}$, $k_{t}$, and $v_{t}$ represent those of the attribute embeddings. The deep fusion of vision and language offers two key benefits: 1) Mask representations are enriched with textual information from language models, resulting in more context-aware segmentation. 2) Text embeddings enhance their descriptive capabilities by attending to visual features, enabling segmentation conditioned on specific text prompts.

We leverage both enhanced mask tokens and text embeddings to generate predictions. By capitalizing on fine-grained vision-language alignment, we formulate semantic segmentation as a matching problem between representative multi-modal query embeddings and pixel-level image features.
Given the $N$ enhanced mask tokens $\hat{m}\in\mathbb{R}^{N\times C}$ and text embeddings $\hat{t}\in\mathbb{R}^{N\times C}$, as well as the output pixel-level image features $F^{4}\in\mathbb{R}^{H\times W\times D\times C}$ from the last 3D image decoder layer, we apply linear projections $\phi$
to generate $Q$(query) and $K$(key) embeddings as:

$$ $\displaystyle Q_{m}=\phi_{m}(\hat{m})\in\mathbb{R}^{N\times C},\quad Q_{t}= \phi_{t}(\hat{t})\in\mathbb{R}^{N\times C},\quad K=\phi_{k}(F^{4})\in\mathbb{R }^{H\times W\times D\times C}.$ (4) $$

Then, the mask predictions could be calculated by the scaled dot product attention:

$$ $\displaystyle Mask_{m}=\frac{Q_{m}K^{T}}{\sqrt{C_{k}}}\in\mathbb{R}^{H\times W \times D\times N},\quad Mask_{t}=\frac{Q_{t}K^{T}}{\sqrt{C_{k}}}\in\mathbb{R}^ {H\times W\times D\times N}$ (5) $$

Here, $Mask_{m}$ and $Mask_{t}$ refer to the two-branch output mask predictions derived from mask tokens and text embeddings, respectively. $\sqrt{C_{k}}$ is the dimension of the keys as a scaling factor. We ensemble these mask predictions as $Mask=MLP(\beta_{1}Mask_{m}+\beta_{2}Mask_{t})$. Here $\beta_{1}$ and $\beta_{2}$ are weighting factors for the vision and language branches, respectively, set to $0.5$ by default. The final segmentation results are obtained by applying the Argmax operation along the channel dimension of the $Mask$.

### 3.3 Training Objectives and Inference

Overall Loss Function: We adopt a composite loss function that balances mask-attribute alignment and mask segmentation. Specifically, we use the dice loss ${\mathcal{L}}_{dice}^{i}$ to supervise the segmentation predictions of mask tokens matched with ground truth at each feature level $i$, achieving deep supervision: ${\mathcal{L}}_{deep}=-\frac{1}{L}\sum_{i=1}^{L}{\mathcal{L}}_{dice}^{(i)}$.
Additionally, we have the similarity loss ${\mathcal{L}}_{sim}$ defined by Eq. (2) for aligning mask tokens with attribute embeddings. For the final segmentation results generated by both visual and textual features, we utilize the binary cross-entropy loss and dice loss: ${\mathcal{L}}_{seg}=\alpha_{1}{\mathcal{L}}_{ce}+\alpha_{2}{\mathcal{L}}_{dice}$. We set $\alpha_{1}=2.0$ and $\alpha_{2}=2.0$. The overall loss function is a weighted sum of these components:

$$ ${\mathcal{L}}=\lambda_{1}{\mathcal{L}}_{deep}+\lambda_{2}{\mathcal{L}}_{sim}+ \lambda_{3}{\mathcal{L}}_{seg}.$ (6) $$

where $\lambda_{1}$, $\lambda_{2}$, and $\lambda_{3}$ are weighting factors balancing the contribution of each
loss component to the overall training objective. We set
$\lambda_{1}=\lambda_{2}=\lambda_{3}=1.0$ as default.

Inference:
Fig. 1 (b) illustrates the model testing flow. To enable convenient and flexible inference, we store the extracted text features of all visual attribute descriptions after training, eliminating the need for the text encoder during testing and thereby reducing computational overhead. The $N$ mask tokens first interact with image features from the 3D image decoder to identify all potential lesion regions. Next, by calculating the similarity between mask tokens and stored text features, we match the mask tokens with their corresponding attribute embeddings (selecting the highest similarity score for each attribute aspect). These matched visual and textual tokens are then fed into the CMKI module to generate the final segmentation results. Based on the matched eight different visual attributes, which can essentially be viewed as diagnostic criteria axes, we obtain the class information for each predicted lesion mask by referencing a clinical knowledge table that records the correspondence between each disease and its eight visual attributes.

## 4 Experiments

### 4.1 Experiment Setup

Datasets.
We utilize annotated datasets encompassing lesions from $12$ diseases and $6$ organs, sourced from both public and private datasets. Specifically, we consider the MSD dataset (Antonelli et al., ) and KiTS23 (Heller et al., ) dataset. We also collect a private dataset that includes four lesion types: liver cyst, gallbladder tumor, gallstone, and kidney stone.
For training, we use the following datasets: 1) colon tumor, lung tumor, liver tumor, and pancreas tumor from the MSD dataset; 2) kidney cyst from the KiTS23 dataset; 3) gallstone from our private dataset. For zero-shot segmentation testing, we adopt: 1) hepatic vessel tumor and pancreas cyst from the MSD dataset; 2) kidney tumor from the KiTS23 dataset; and 3) liver cyst, gallbladder tumor and kidney stone from our private dataset.
Details of all datasets, the annotation process for disease attribute descriptions, and preprocessing are provided in Appendix B.

Evaluation Metrics.
We adopt standard segmentation metrics, including the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD). Additionally, we report the the computational efficiency evaluation, which are detailed in Appendix E.

Implementation Details.
We use nnUNet (Isensee et al., ) as the backbone for 3D image encoder and 3D image decoder. We choose Clinical-Bert (Alsentzer et al., ) as the text encoder.
We employ AdamW optimizer (Loshchilov & Hutter, ) with a warm-up cosine scheduler of $40$ epochs. The batch size is set to $2$ per GPU. Each input volume is cropped into patches with a size of $96\times 96\times 96$. The training process uses an initial learning rate of $1e^{-4}$, momentum of $0.9$, and weight decay of $1e^{-5}$, running on $8$ NVIDIA A$100$ GPUs with DDP for $4000$ epochs. The number of the mask tokens $N$ is set as $16$.

### 4.2 Main Results

Zero-shot segmentation performance on unseen lesions.
Table 1 presents the zero-shot lesion segmentation results on unseen datasets from various institutions, encompassing a wide range of lesion types. All available CT volumes in these tasks are directly used for testing. We compare Malenia with five state-of-the-art zero-shot medical image segmentation methods: the self-prompted ZePT (Jiang et al., ), the prompt-free SAM-based method H-SAM (Cheng et al., ), the fine-tuning-free method combining CLIP and SAM, SaLIP (Aleem et al., ), and two methods (Shaharabany & Wolf, ; Yamagishi et al., ) that respectively adopt SAM and SAM 2.
Malenia achieves superior performance across all the datasets, substantially outperforming the other five methods.
The weak performance of SAM (Shaharabany & Wolf, ) and SAM 2 (Yamagishi et al., ) can be attributed to the inherent variability of segmentation tasks in different clinical scenarios. Without target data for fine-tuning and accurate manual prompts, the SAM and SAM 2 struggle with lesion segmentation in the zero-shot setting. Additionally, creating accurate prompts requires domain knowledge from medical experts, which is often limited and time-consuming.
SaLIP leverages the capabilities of CLIP to refine automatically generated prompts for SAM. However, its performance is still constrained by CLIP, which focuses on aligning image-level global features while neglecting fine-grained local lesion semantics, resulting in suboptimal performance for lesion segmentation. H-SAM achieves better results by incorporating enhanced mask attention mechanisms in a two-stage decoding process. However, it does not explore cross-modal feature representations. Without the rich textual knowledge from language models, its zero-shot segmentation performance is significantly hindered.
ZePT is trained using both organ and lesion labels to generate anomaly score maps as prompts, and it aligns mask features with common medical knowledge to enhance zero-shot tumor segmentation. However, it overlooks the patient-specific multi-aspect elemental attributes shared across different diseases, which limits the scalability and generalization of its learned representations. In contrast, Malenia learns fine-grained lesion-level mask-attribute alignment to link unseen lesions with base knowledge and leverages both visual and textual context for advanced cross-modal understanding, resulting in at least a $6.40\%$ improvement in DSC on MSD, a $8.14\%$ improvement in DSC on KiTS23, and a $9.08\%$ improvement in DSC on the in-house dataset.

**Table 1: Segmentation performance (%) of unseen lesions on the MSD, KiTS23, and our in-house dataset. ${\dagger}$ denotes methods that adopt SAM or SAM2 for zero-shot medical image segmentation, and $*$ indicates methods implemented using the official code. All competing methods are trained on the same dataset. The best performance is highlighted in light blue.**
| Method | MSD | KiTS23 | In-house Dataset |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Hepatic Vessel Tumor | Pancreas Cyst | Kidney Tumor | Liver Cyst | Kidney Stone | Gallbladder Tumor |  |  |  |  |  |  |  |
| DSC$\uparrow$ | NSD$\uparrow$ | DSC$\uparrow$ | NSD$\uparrow$ | DSC$\uparrow$ | NSD$\uparrow$ | DSC$\uparrow$ | NSD$\uparrow$ | DSC$\uparrow$ | NSD$\uparrow$ | DSC$\uparrow$ | NSD$\uparrow$ |  |
| SAM${\dagger}$ (Shaharabany & Wolf, ) | 35.76 | 45.83 | 37.17 | 49.26 | 35.45 | 41.33 | 34.99 | 40.88 | 24.14 | 31.92 | 28.08 | 36.38 |
| SAM2${\dagger}$ (Yamagishi et al., ) | 35.93 | 45.88 | 38.42 | 50.85 | 35.67 | 41.88 | 35.29 | 41.25 | 25.50 | 33.74 | 28.57 | 36.62 |
| SaLIP$*$ (Aleem et al., ) | 39.65 | 48.71 | 41.92 | 53.06 | 38.64 | 44.91 | 37.71 | 44.26 | 27.24 | 36.61 | 30.84 | 38.97 |
| H-SAM* (Cheng et al., ) | 45.58 | 54.24 | 46.87 | 57.91 | 44.21 | 50.39 | 43.75 | 50.20 | 29.23 | 38.11 | 32.17 | 40.05 |
| ZePT$*$ (Jiang et al., ) | 53.12 | 63.25 | 53.35 | 63.50 | 46.82 | 52.44 | 51.64 | 57.36 | 33.97 | 42.42 | 35.48 | 43.23 |
| Malenia | 59.52 | 69.60 | 60.91 | 70.28 | 54.96 | 60.60 | 61.85 | 70.93 | 43.05 | 52.95 | 47.35 | 55.79 |

Segmentation performance on seen lesions.
We also evaluate the segmentation performance of Malenia on seen lesions in the fully-supervised setting. We compare Malenia with SOTA medical image segmentation methods, including TransUNet (Chen et al., ), nnUNet (Isensee et al., ), Swin UNETR (Tang et al., ), and the Universal Model (Liu et al., ).
As shown in Table 2, Malenia outperforms competing baselines on seen lesions, achieving an average improvement of $1.92\%$ in DSC for tumors on the MSD dataset (Antonelli et al., ) and $1.35\%$ in DSC for kidney cysts on KiTS23 (Heller et al., ). Most segmentation baselines (e.g., TransUNet,
nnUNet, and Swin UNETR) focus solely on visual features and overlook the semantic relationships between different lesions. Although the Universal Model incorporates text embeddings of category names to help learn correlations between anatomical regions, it still lacks fine-grained information from both the vision and language domains. In contrast, Malenia outperforms these methods by aligning fine-grained lesion-level semantics with comprehensive, patient-specific textual features and enhancing mask representations through cross-modal knowledge injection. This improvement demonstrates that our novel strategies also enhance the segmentation of seen lesion categories.

Qualitative Comparison. As shown in Fig. 2, Malenia accurately segments various types of lesions across diverse organs, having substantially better performance in segmenting both seen and unseen lesions than the other methods. Most competing methods suffer from incomplete segmentation and false positives. In contrast, Malenia produces results that are more consistent with the ground truth. This further demonstrates the superior lesion segmentation capability of Malenia on datasets with highly diverse lesion semantics.

**Table 2: Segmentation performance (%) of seen lesions on the MSD and KiTS23 dataset. We compare Malenia with SOTA supervised methods and report the 5-fold cross-validation results. $*$ means implemented from the official code and trained on the same dataset. The best performance is highlighted in light blue.**
| Method | MSD | KiTS23 |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Colon Tumor | Pancreas Tumor | Liver Tumor | Lung Tumor | Kidney Cyst |  |  |  |  |  |  |
| DSC$\uparrow$ | NSD$\uparrow$ | DSC$\uparrow$ | NSD$\uparrow$ | DSC$\uparrow$ | NSD$\uparrow$ | DSC$\uparrow$ | NSD$\uparrow$ | DSC$\uparrow$ | NSD$\uparrow$ |  |
| TransUNet$*$ | 44.78±16.21 | 54.14±15.67 | 38.85±10.25 | 54.72±11.59 | 60.05±5.29 | 72.88±5.98 | 53.39±6.76 | 55.94±7.35 | 48.43±14.04 | 52.32±15.62 |
| nnUNet$*$ | 47.02±15.85 | 57.36±14.33 | 37.97±10.54 | 53.98±11.86 | 61.33±5.01 | 73.27±5.44 | 54.44±6.30 | 56.48±7.17 | 48.76±13.82 | 52.96±15.19 |
| Swin UNETR$*$ | 46.87±16.02 | 55.28±15.52 | 38.72±10.33 | 54.01±11.67 | 62.37±4.88 | 74.75±5.09 | 55.65±6.18 | 57.98±6.54 | 48.06±14.26 | 52.11±16.05 |
| Universal Model$*$ | 51.02±14.62 | 60.93±13.36 | 42.40±9.54 | 58.54±10.79 | 64.25±3.94 | 77.06±4.21 | 54.51±6.27 | 56.68±7.11 | 50.25±12.24 | 54.17±13.53 |
| Malenia | 53.55±13.49 | 62.41±12.81 | 43.30±9.29 | 59.63±10.55 | 65.18±3.74 | 78.95±4.03 | 57.84±5.53 | 59.97±5.90 | 51.60±11.84 | 55.41±12.99 |

Figure: Figure 2: Qualitative visualizations of Malenia and other competing methods on both unseen and seen lesions. The segmentation results, presented from top to bottom and left to right, include Hepatic Vessel Tumor, Pancreas Cyst, Colon Tumor, and Lung Tumor.
Refer to caption: x2.png

### 4.3 Ablation Studies

Multi-scale mask-attribute alignment strategy ablation.
We validate the effectiveness of different components of the multi-scale mask-attribute alignment strategy on both seen and unseen lesion datasets, as detailed in Table 4. ‘Baseline’ refers to the naive single-scale mask-report alignment performed at the last Transformer decoder block.
We gradually enhance the baseline by ($S_{1}$) enriching raw reports with structured eight visual attribute descriptions of the lesions; ($S_{2}$) utilizing multi-scale features for cross-modal alignment; ($S_{3}$) formulating mask-attribute alignment as multi-positive contrastive learning using multiple mask-attribute positive pairs, rather than a single mask-report positive pair. Each component contributes to the remarkable segmentation performance of Malenia. Enriching raw reports with visual attributes of lesions ($S_{1}$) helps the model leverage pre-established knowledge of the diseases’ visual manifestations to enhance the alignment of fine-grained image features with the representations of target diseases, thereby improving segmentation performance.
Multi-scale cross-modal alignment ($S_{2}$) leverages multi-level features to accurately capture and segment both seen and unseen lesions across various sizes, which is essential for handling the shape and size variations of lesions.
Furthermore, instead of combining the eight visual attribute descriptions of each lesion into a single paragraph and then extracting the text features ($S_{1}$), we extract the text features for each visual attribute description separately ($S_{3}$). As a result, the mask embeddings for each lesion is paired with eight distinct text features, forming multiple positive sample pairs. Simply treating a lesion’s attribute descriptions as a single paragraph yields only one positive sample pair for each foreground mask token. Consequently, reports of other lesions that share some of the same attribute descriptions are treated as negative samples, leading to compromised feature representations. In contrast, our formulation of the multi-positive contrastive learning ($S_{3}$) focuses on establishing comprehensive and extensible correlations between pathological features and fundamental disease attributes. This enables the model to associate the visual cues of unseen lesions with foundational visual knowledge, allowing for effective segmentation of new diseases by translating their complex visual features into elemental attributes shared with seen diseases. This significantly enhances zero-shot lesion segmentation performance.

**Table 3: Ablation study of the multi-scale mask-attribute alignment strategy.**
| Module | MSD (seen) | KiTS23 (unseen) | In-house Dataset (unseen) |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
| Pancreas Tumor | Kidney Tumor | Gallbladder Tumor |  |  |  |  |
| DSC$\uparrow$ | NSD$\uparrow$ | DSC$\uparrow$ | NSD$\uparrow$ | DSC$\uparrow$ | NSD$\uparrow$ |  |
| Baseline | 38.77±10.30 | 54.28±11.63 | 47.05 | 53.47 | 36.12 | 43.81 |
| $+S_{1}$ | 39.83±10.13 | 55.66±11.47 | 48.69 | 54.82 | 38.36 | 46.20 |
| $+S_{2}$ | 40.46±9.95 | 56.52±11.18 | 49.63 | 55.99 | 39.74 | 47.79 |
| $+S_{1}+S_{2}$ | 41.88±9.88 | 57.79±11.05 | 51.85 | 57.88 | 42.53 | 50.67 |
| $+S_{1}+S_{3}$ | 42.23±9.71 | 58.24±10.96 | 53.54 | 59.46 | 44.40 | 52.91 |
| $+S_{1}+S_{2}+S_{3}$ | 43.30±9.29 | 59.63±10.55 | 54.96 | 60.60 | 47.35 | 55.79 |

Figure: Figure 3: Visualization illustrating how utilizing both mask and text embeddings improves segmentation performance for both unseen and seen lesions. We present several prediction cases generated using both text embeddings and mask tokens, text embeddings only (TE), and mask tokens only (MT).
Refer to caption: x3.png

Ablation of the Cross-Modal Knowledge Injection module.
We examine key components in the proposed Cross-Modal Knowledge Injection module in detail, including (1) the significance of deep fusion (DF), which is designed to enhance the representation of mask tokens and text embeddings; (2) the effectiveness of leveraging both text embeddings (TE) and mask tokens (MT) to generate predictions. The results are shown in Table 4. Comparing the results of the first three rows with the last three rows (highlighted in light red), it is evident that deep fusion significantly improves performance, whether using only text embeddings, only mask tokens, or both for segmentation result prediction. This observation shows the importance of enabling cross-modal information interaction for optimal performance. Furthermore, whether or not deep fusion is applied, using both text embeddings and mask tokens for segmentation prediction, combined with ensembling the results, consistently outperforms using only unimodal token embeddings for segmentation prediction.
This confirms the superiority of leveraging the complementary strengths of both visual and textual embeddings to further enhance segmentation performance.
Fig. 3 provides detailed examples illustrating how combining text tokens and mask tokens improves performance. While using mask tokens for segmentation prediction is more intuitive and generally yields better performance than using text embeddings alone (as shown in Table 4), we observed that when lesion boundaries are particularly blurry (as seen in the Hepatic Vessel Tumor cases on the left side of Fig. 3), the visual features from the mask tokens fail to accurately capture the lesion’s area. In such instances, text descriptions of attributes—such as the lesion’s shape and surface characteristics—provide additional information. The segmentation generated by the corresponding text embeddings refines the results produced by mask tokens alone, leading to improved performance. Moreover, we observed that, owing to the clear semantic information and specific contexts carried by the textual features of attribute descriptions, the segmentation results generated using attribute embeddings exhibit significantly fewer false positives. This further refines the segmentation produced by mask tokens by reducing false positives, as demonstrated in the colon tumor cases on the right side of Fig. 3.

## 5 Conclusion

In this work, we propose Malenia, a novel vision-language pre-training method designed for 3D zero-shot lesion segmentation, which incorporates an innovative multi-scale lesion-level vision-language alignment strategy.
Inspired by the image interpretation process of human experts, we transform patient-specific reports into structured descriptions of disease visual attributes and then match them with mask representations.
Additionally, we introduce a novel Cross-Modal Knowledge Injection module, which enhances cross-modal representations and leverages the complementary strengths of both visual and textual features to further improve segmentation performance. Extensive experiments demonstrate that Malenia consistently outperforms previous state-of-the-art approaches across diverse datasets for 3D zero-shot lesion segmentation. We hope this work inspires further innovation in this challenging yet promising research area.

## Appendix A Preliminaries

Problem Formulation of Zero-Shot Lesion Segmentation.
Traditional semantic segmentation is inherently limited to a closed-set setting and often struggles in real-world applications where there can be anomalous categories in the test data unseen during training.
In contrast, the goal of zero-shot segmentation is to segment objects belonging to categories that have not been encountered during training. In this work, we explore the zero-shot setting for lesion segmentation in 3D CT images. Specifically, there are two non-overlapping foreground sets: (1) $N^{s}$ seen (base) categories of lesions denoted as $C^{s}$;
(2) $N^{u}$ unseen (novel) categories of lesions denoted as $C^{u}$, $C^{s}\cap C^{u}=\varnothing$.
The training data is constructed from the images and labels that contain any of the $N^{s}$ seen categories. Among these images, there may also exist unseen categories from $C^{u}$, whose annotations can not be accessed during the training. Malenia aims to segment lesions, both seen and unseen, in the test data.

Mask Tokens.
Recently, a transformer-based segmentation model Mask2Former (Cheng et al., ) that can generate a set of segment-level embeddings have been applied and achieved promising performance in the field of medical image segmentation (Yuan et al., ; Chen et al., ; Jiang et al., ). Different from standard segmentation methods, Mask2Former adopt an additional lightweight transformer decoder along with a vision encoder and a pixel decoder. By feeding $N$ mask tokens (also called segment queries) and pixel decoder features into the transformer decoder, Mask2Former generates $N$ segment-level embeddings with masked attention layers, effectively partitioning an image into $N$ corresponding regions. The final binary mask segmentations are obtained by computing the dot product of the mask tokens with the image features from the last pixel decoder layer, while an additional MLP predicts the class for each mask. In contrast, this work extends the closed-set setting of mask classification to an open-set setting through fine-grained mask-text alignment. Specifically, we propose enhancing and matching mask tokens with text embeddings of the visual attributes of pathologies within multi-scale transformer decoder blocks. Additionally, we leverage the complementary strengths of visual and text representations for mask refinement, demonstrating strong generalizability and improved zero-shot lesion segmentation performance.

## Appendix B Dataset Details and Data Preprocessing

### B.1 Dataset Details

Our study utilizes annotated datasets encompassing lesions across $12$ diseases and $6$ organs, derived from both public and private sources. The private datasets particularly include paired CT scans and radiological reports providing systematic imaging descriptions of lesions. We summarize all the datasets in Table 5.

Public Datasets:

- •
Kidney Tumor and Kidney Cyst. This dataset is part of the Kidney and Kidney Tumor Segmentation Challenge (KiTS23) (Heller et al., ), which provides $489$ cases of data with annotations for the segmentation of kidneys, renal tumors, and cysts.
- •
Liver Tumor, Pancreas Tumor, Pancreas Cyst, Colon Tumor, and Lung Tumor. These datasets are part of the Medical Segmentation Decathlon (MSD) (Antonelli et al., ), providing annotated datasets for various lesions. Notably, Pancreas Tumors and Cysts are grouped under a single region of interest in the MSD dataset but were separately classified by experienced radiologists as part of this study.

Private Datasets:

- •
For lesions underrepresented in public datasets, we utilized private datasets annotated by radiologists. These datasets include lesions such as kidney stones, liver cysts, gallbladder cancer, and gallstones, accompanied by paired radiological reports. Each dataset consists of 30 cases, providing a balanced sample size for preliminary analysis.

### B.2 Creating Structured Disease Attribute Descriptions

Specifically, For the public datasets MSD and KiTS23, which only provide mask annotations, two senior radiologists supplemented the annotated lesions with corresponding lesion descriptions of the eight visual attribute aspects. For the in-house data, we utilized the semi-automatic annotation pipeline described in Sec.3.1. First, we prompted GPT-4(Achiam et al., ) to extract lesion-related descriptions from the findings section of each report. Then, two experienced radiologists collaborated to review, correct, supplement, and expand the GPT-generated visual attribute descriptions. Through this process, we established eight standardized attributes for describing lesions, which form the basis of our structured reports. These attributes provide a comprehensive and objective template for characterizing lesions, ensuring consistency and enabling generalization across different types of lesions, as shown in Table 6.

**Table 5: Details of Datasets. $*$ denotes MSD-Pancreas is reannotated to pancreas tumor and pancreas cyst.**
| Dataset Name | Segmentation Lesion Targets | # of scans |
| --- | --- | --- |
| KiTS23 | Kidney Tumor, Kidney Cyst | 489 |
| MSD-Colon Tumor | Colon Tumor | 126 |
| MSD-Liver Tumor | Liver Tumor | 131 |
| MSD-Hepatic Vessel Tumor | Hepatic Vessel Tumor | 303 |
| MSD-Lung Tumor | Lung Tumor | 64 |
| MSD-Pancreas^∗ | Pancreas Tumor | 216 |
| MSD-Pancreas^∗ | Pancreas Cyst | 65 |
| Private Data | Liver Cyst | 30 |
| Gallbladder Tumor | 30 |  |
| Gallstones | 30 |  |
| Kidney Stone | 30 |  |

**Table 6: Definitions and detailed content of the eight disease attributes.**
| Attribute | Definition | Content |
| --- | --- | --- |
| Enhancement Status | Intravenous contrast agent usage | “Enhanced CT”,“Non-contrast CT” |
| Location | Organ-specific anatomical regions | “Colon”, “Liver”, “Pancreas”, “Right Lung”, “Left Lung”<br>“Right Kidney”, “Left Kidney”, “Gallbladder”, “Hepatic Vessel” |
| Shape | Morphological characteristics<br>of the lesion | “Round-like”, “Irregular”,<br>“Wall thickening”, “Punctate”,<br>“Nodular”, “Cystic”,<br>“Luminal narrowing”,<br>“Protrusion into the lumen” |
| Relationship with<br>Surrounding Organs | Invasion of or proximity<br>to adjacent organs | “No close relationship with surrounding organs”,<br>“Close relationship with adjacent organs” |
| Density | Radiographic attenuation<br>properties of the lesion | “Hypodense lesion”, “Isodense lesion”,<br>“Hyperdense lesion”, “Mixed-density lesion”,<br>“Hypodense fluid-like lesion”,<br>“Isodense soft tissue mass”,<br>“Low-density ground-glass opacity” |
| Density Variations | Uniformity of attenuation<br>within the lesion | “Homogeneous”, “Heterogeneous” |
| Surface Characteristics | Features of the lesion’s border<br>and surface texture | “Well-defined margin”,<br>“Clear serosal surface”,<br>“Ill-defined margin”,<br>“Serosal surface irregularity” |
| Specific Features | Distinctive attributes indicating<br>lesion characteristics | “Spiculated margins”,<br>“Retention of pancreatic fluid”, … |

### B.3 Data Preprocessing

We pre-process CT scans using isotropic spacing and uniformed intensity scale to reduce the domain gap among various datasets.
We determine whether each CT scan is contrast-enhanced by evaluating the Hounsfield unit (HU) values of the aorta and inferior vena cava. If the average HU of both the aorta and inferior vena cava is less than $80$, the scan is classified as non-contrast. Otherwise, it is classified as contrast-enhanced. This helps radiologists determine the attribute description ”Enhancement Status”.

## Appendix C Fine-Tuning Evaluation

Malenia can also serve as a valuable pre-training method for downstream segmentation tasks in a fine-tuning setting. To evaluate this, we adopt various vision-language pre-training strategies to pre-train the 3D image encoder of a nnUNet model using our training data. We then fine-tune the nnUNet on downstream datasets using the pre-trained weights.
For all downstream datasets, we split each into $50\%$ for training, $20\%$ for validation, and $30\%$ for testing.
As shown in Table 7, Malenia consistently outperforms previous methods, further validating the importance of tailoring vision-language pre-training for dense prediction tasks in the medical domain, where a fine-grained understanding of disease-related features is essential. Notably, Malenia significantly surpasses the second-best method, CT-GLIP, by $6.03\%$ and $3.77\%$ in DSC on hepatic vessel tumor and pancreas cyst segmentation, respectively. On our in-house dataset, Malenia also delivers a substantial performance boost. The fine-tuning evaluations demonstrate that injecting fine-grained, lesion-level cross-modal knowledge from both the vision and language domains to enhance mask representations during pre-training improves the model’s transferability, particularly for fine-grained lesion segmentation tasks.

**Table 7: Segmentation results of different vision-language pretraining models under the fine-tuning setting.**
| Method | MSD | KiTS23 | In-house Dataset |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Hepatic Vessel Tumor | Pancreas Cyst | Kidney Tumor | Liver Cyst | Kidney Stone | Gallbladder Tumor |  |  |  |  |  |  |  |
| DSC$\uparrow$ | NSD$\uparrow$ | DSC$\uparrow$ | NSD$\uparrow$ | DSC$\uparrow$ | NSD$\uparrow$ | DSC$\uparrow$ | NSD$\uparrow$ | DSC$\uparrow$ | NSD$\uparrow$ | DSC$\uparrow$ | NSD$\uparrow$ |  |
| nnUNet (from Scratch) | 62.44 | 72.87 | 64.15 | 76.27 | 70.59 | 76.79 | 67.84 | 74.03 | 50.47 | 59.91 | 56.70 | 64.68 |
| GLoRIA (Huang et al., ) | 63.11 | 73.02 | 64.55 | 76.41 | 72.03 | 77.82 | 68.35 | 74.87 | 50.82 | 60.25 | 57.53 | 65.46 |
| BioViL (Boecking et al., ) | 63.14 | 73.08 | 64.57 | 76.44 | 72.10 | 77.94 | 68.66 | 75.02 | 50.88 | 60.27 | 57.62 | 65.69 |
| MedKLIP (Wu et al., ) | 64.30 | 74.45 | 65.20 | 77.35 | 72.87 | 78.71 | 69.15 | 75.56 | 51.42 | 61.65 | 58.34 | 66.40 |
| MAVL (Phan et al., ) | 65.49 | 75.51 | 65.49 | 77.61 | 74.09 | 80.23 | 69.23 | 75.60 | 52.30 | 62.14 | 58.71 | 66.77 |
| CT-GLIP (Lin et al., ) | 65.93 | 75.97 | 66.18 | 78.06 | 74.15 | 80.26 | 69.92 | 76.11 | 52.67 | 62.52 | 58.93 | 66.95 |
| Malenia | 71.96 | 81.83 | 69.95 | 82.00 | 76.88 | 82.55 | 72.44 | 79.84 | 53.35 | 63.69 | 64.21 | 72.63 |

## Appendix D Evaluation of Lesion-Attribute Matching

Mask-attribute alignment is the core foundation of our method. In this section, we report the zero-shot lesion-attribute matching performance of Malenia. Table 8 presents the lesion-level mask-attribute matching precision and recall for each attribute aspect on the zero-shot testing dataset. Malenia achieved outstanding zero-shot performance in the task of lesion-attribute alignment. Notably, we observed that for attributes that are relatively easier to determine, such as “Enhancement Status” and “Location”, the model attained nearly $100\%$ precision and recall. However, for attributes requiring more complex visual semantic understanding, such as “Surface Characteristics”, “Density”, and “Specific Features”, there is still room for improvement. This may represent a meaningful research direction for future work. In Fig. 4, we present visualizations of some of Malenia’s segmentation and attribute alignment results.

**Table 8: Zero-shot lesion-attribute matching performance ($\%$) of Malenia.**
| Attribute Aspect | MSD | KiTS23 | In-house Dataset |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Hepatic Vessel Tumor | Pancreas Cyst | Kidney Tumor | Liver Cyst |  |  |  |  |  |
| Precision$\uparrow$ | Recall$\uparrow$ | Precision$\uparrow$ | Recall$\uparrow$ | Precision$\uparrow$ | Recall$\uparrow$ | Precision$\uparrow$ | Recall$\uparrow$ |  |
| Enhancement Status | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
| Location | 99.34 | 99.67 | 98.46 | 96.92 | 99.68 | 99.25 | 100 | 100 |
| Shape | 95.71 | 97.03 | 89.23 | 90.77 | 94.27 | 93.67 | 86.67 | 90.00 |
| Relationship with Surrounding Organs | 99.01 | 98.35 | 96.92 | 98.45 | 99.18 | 98.57 | 93.34 | 96.67 |
| Density | 92.41 | 93.40 | 92.31 | 93.76 | 94.35 | 94.87 | 92.63 | 92.11 |
| Density Variations | 95.62 | 96.13 | 93.04 | 92.81 | 94.52 | 93.79 | 90.00 | 93.34 |
| Surface Characteristics | 91.05 | 91.62 | 89.83 | 90.45 | 92.44 | 93.83 | 88.21 | 88.06 |
| Specific Features | 84.54 | 85.46 | 82.13 | 82.40 | 83.29 | 82.96 | 82.75 | 83.34 |

Figure: Figure 4: Visualization of segmentation and lesion-attribute matching results of Malenia. The numbers indicate the normalized similarity scores between the lesion masks and the corresponding attributes.
Refer to caption: x4.png

## Appendix E Computational Cost Comparison

We compare the number of parameters across different methods to evaluate computational efficiency.
Moreover, we also assessed the inference speed of various models, as this is crucial for clinical applications. We utilized floating-point operations per second (FLOPs) as a metric to measure the inference speed. Table 9 presents the number of parameters and FLOPs (G) for Malenia and the comparison models, demonstrating that our method not only achieves the best performance but also superior computational efficiency. Remarkably, due to the mask-attribute alignment mechanism, Malenia directly matches mask embeddings with stored attribute embeddings and does not require an additional text encoder to process text inputs during inference. This makes it more flexible and efficient than language-driven segmentation models like ZePT (Jiang et al., ), which rely on user-provided text prompts based on the input images.
Additionally, Malenia does not require an extra encoder to process complex visual prompts. Instead, it directly generates segmentation predictions for the input image while also producing attribute descriptions. This approach is more aligned with real clinical diagnostic scenarios, where doctors expect the AI model to first provide diagnostic predictions to assist in the diagnostic process.

**Table 9: Computational cost comparison between Malenia and competing methods used in our evaluation experiments. The FLOPs are computed based on an input with a spatial size of $96\times 96\times 96$ on the same A100 GPU.**
| Efficiency<br>Method | Malenia | ZePT | H-SAM | nnU-Net | Swin UNETR |
| --- | --- | --- | --- | --- | --- |
| Params | 323.86M | 745.94M | 557.60M | 370.74M | 371.94M |
| FLOPs | 1128.96G | 1337.59G | 3643.96G | 6742.36G | 2005.48G |

## Appendix F Limitations and Future Work

Malenia currently achieves superior zero-shot lesion segmentation performance, approaching that of fully-supervised, task-specific models. However, zero-shot lesion segmentation across anatomical regions and imaging modalities remains highly challenging. For instance, training on lesion data from abdominal CT and directly testing on brain cancer MRI is extremely challenging due to the significant differences in anatomical structures and feature distribution between the training and testing images. We believe that achieving cross-domain and cross-modality zero-shot generalization is a challenging yet meaningful research direction in both natural and medical imaging domains. This will be the focus of our future work.

## Peer Reviews

confidence: 3
contribution: 3
initial_rating: 5
presentation: 3
questions: Considering the practicality issue, would replacing GPT-4 with an open-source LLM for attribute construction (probably also without human annotators?) reaching a similar level of performance gain (as mentioned above)?
soundness: 3
strengths: The idea of decomposing free-text reports into attributes for fine-grain training and inference is intuitive and feasible. It can significantly reduce the noise and ambiguity associated with unstructured free-text reports and yields improved performance on unseen lesions.

The key components and the detailed implementations are described in good detail. 

The proposed approach demonstrates performance improvements compared with previous zero-shot image segmentation methods.

Detailed ablation studies are presented to verify the effectiveness of key components.
summary: This work presents a zero-shot medical image semantic segmentation framework that is based on maskformer and attribute-based visual-language feature fusion. To achieve fine-grain alignment between visual and textual concepts, the authors propose to decompose radiological reports into attributes describing lesions of interest, assisted by LLMs and human annotators. The obtained attributes allows fine-grain fusion and alignment between lesion features and textual concepts, yielding improved generalization performance upon unseen lesions. The proposed framework is evaluated on tumor segmentation tasks, and demonstrates improved segmentation results compared with existing zero-shot segmentation approaches on unseen lesions.
weaknesses: Despite stronger segmentation performance, the major framework looks similar to ZePT (Jiang et al., 2024): maskformer backbone + interacting visual and textual features that are obtained from detailed description of the lesions. Therefore, the key take-home message for readers may be a bit unclear: should the readers interpret the key technical contribution as explicitly decomposing textual descriptions/reports into categorized attributes for the ease of learning and inference?

Readers may argue that using GPT-4 for attribute construction from radiological reports is often infeasible in practice due to the privacy and legal concerns. The authors are encouraged to argue if switching to local open-source LLMs would yield a similar level of performance gain. 

The authors may want to decompose some of technical details in Figure 1 into separate figures and move them closer to corresponding paragraphs.

---

confidence: 5
contribution: 2
initial_rating: 5
presentation: 4
questions: please refer to Weaknesses
soundness: 2
strengths: 1. The topic on zero-shot lesion segmentation is valuable, as clinical settings often involve diverse, emerging anomalies, and data collection is challenging, increasing the need for models that handle unseen diseases in an open-set context.
2. The authors strengthen their claims with both qualitative and quantitative results, enhancing the credibility of their findings and providing a well-rounded evaluation of the proposed approach.
3. The paper is well-organized, with a clear and logical structure that makes complex concepts accessible and easy to follow for readers.
summary: This paper introduces Malenia, a framework for zero-shot lesion segmentation in 3D medical images, aimed at addressing the challenge of transferring image-level knowledge to pixel-level segmentation tasks. Building on advancements in medical vision-language pre-training, Malenia integrates a multi-scale mask-attribute alignment framework and a Cross-Modal Knowledge Injection module to link visual and textual features, enabling the model to handle previously unseen lesions. The authors evaluate Malenia's performance through experiments on three datasets (two public and one private) across 12 lesion categories.
weaknesses: 1. The Zero-Shot Inference section lacks sufficient detail on how the model handles test CT images containing unseen tumor types. For instance, if a test image includes both a kidney tumor and a gallbladder tumor, it is unclear how the model predicts these different tumors within the same image. Additionally, the phrase “we obtain the class information for each predicted lesion mask by referencing a clinical knowledge table” is ambiguous. Further clarification is needed on what this clinical knowledge table entails and how it is used in the inference process.
2. The comparisons in Table 1 and Table 2 may not be entirely fair. The authors introduce additional ATTRIBUTE DESCRIPTIONS annotations, which provide extra information not available to baseline methods like TransUNet, nnUNet, and Swin UNETR. These baselines rely solely on image data, while the proposed approach leverages attribute annotations, giving it an advantage in performance comparisons.
3. The fully-supervised performance of baseline methods reported in Table 2 appears unusually low. For instance, in the official nnUNet paper, liver tumor segmentation achieved a Dice score of 76 on the test set, whereas the presented result here is only 61.33. Similarly, lung tumor segmentation originally reported a Dice score of 74, but this paper reports 54.44. This discrepancy raises concerns about the reproducibility and fairness of the comparisons.
4. In Table 7, the authors compare their method to existing vision-language pretraining strategies, but it is unclear how these methods were reproduced. Most vision-language pretraining approaches require access to diagnostic reports, which are not included in the public MSD and KiTS23 datasets. It would be helpful to understand how the authors conducted these comparisons without complete diagnostic reports. Additionally, the absence of comparisons with important pretraining models like CT-CLIP weakens the evaluation.
5. Scalability of the proposed method is questionable due to its reliance on ATTRIBUTE DESCRIPTIONS. For practical clinical applications, this approach would require physicians to provide additional lesion descriptions across eight visual attribute aspects, which may not be feasible in all settings. This dependency could limit the method’s applicability in diverse clinical environments.

---

confidence: 5
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. Could the authors provide insights on the computational efficiency of their framework?
2. This work and its motivation are similar to the previous works [1-2], please describe the differences between them.
[1] Li, Z., Li, Y., Li, Q., Wang, P., Guo, D., Lu, L., ... & Hong, Q. (2023). Lvit: language meets vision transformer in medical image segmentation. IEEE transactions on medical imaging.
[2] Huang, X., Li, H., Cao, M., Chen, L., You, C., & An, D. (2024). Cross-Modal Conditioned Reconstruction for Language-guided Medical Image Segmentation. arXiv preprint arXiv:2404.02845.
soundness: 3
strengths: 1. This study bridges the gap between mask representations and textual disease attributes for zero-shot lesion segmentation.
2. The empirical validation includes comprehensive experiments across multiple datasets and lesion types, demonstrating the robustness and effectiveness of Malenia.
summary: The paper introduces "Malenia," a framework for zero-shot 3D lesion segmentation using vision-language pre-training methods. This framework aligns multi-scale mask representations of lesions with textual embeddings of disease attributes, which facilitates the segmentation of unseen lesion types in medical imaging.
weaknesses: 1. While the paper effectively addresses 3D CT scans, it does not discuss the applicability of the Malenia framework to other imaging modalities like X-ray.
2. The paper does not discuss how the framework handles ambiguous or borderline cases where lesion boundaries are not well-defined or where lesions exhibit highly atypical appearances.
3. There is no thorough discussion on the limitations or failures of the proposed model.

---

confidence: 4
contribution: 2
initial_rating: 6
presentation: 2
questions: 1. In Section 3.1, the authors employ the Multi-Positive InfoNCE (MP-NCE) loss for aligning mask embeddings with multiple attribute embeddings. Could the authors provide more details on how the MP-NCE loss is computed and how it facilitates learning with multiple positive pairs? Specifically, how are the positive and negative pairs defined in the context of multiple attributes?
   Could the authors explain how this formulation effectively handles multiple positive pairs for each mask token?
2. The model uses a fixed number of mask tokens ($N=16$) and defines $R=8$ attribute aspects. Have the authors conducted any experiments to assess how varying $N$ or the number of attributes affects the model's performance? An ablation study on these hyperparameters could provide insights into the model's sensitivity and optimal settings.
3. As noted in Appendix D, the model performs less well on attributes like "Surface Characteristics" and "Specific Features". What are the potential reasons for this lower performance? Could incorporating additional contextual information or advanced features help the model better capture complex visual semantics?
soundness: 2
strengths: 1. The proposed Malenia framework is novel, particularly in its use of multi-scale mask-attribute alignment for 3D lesion segmentation. This is a meaningful extension of zero-shot segmentation methods to the medical domain, where unseen lesion categories are common (e.g., "hepatocellular carcinoma," mentioned in Sec. 1).
2. The CMKI module is a strong contribution because it combines visual and textual embeddings, enriching the segmentation prediction with complementary information from both modalities. This is a novel approach that enhances the zero-shot capability of the model.
3. The paper introduces a multi-positive contrastive loss to fine-tune the alignment between lesion masks and textual attributes. This approach allows the model to generalize better to unseen lesions by learning from shared visual attributes, which distinguishes it from prior works like SAM (Kirillov et al., 2023).
summary: The paper introduces Malenia, a novel framework specifically designed for 3D zero-shot lesion segmentation in medical images. The authors aim to address the challenges of transferring image-level knowledge to pixel-level tasks—such as lesion segmentation—by proposing a multi-scale lesion-level mask-attribute alignment approach. Malenia improves the alignment between the visual features of unseen lesions with textual representations, leveraging Cross-Modal Knowledge Injection (CMKI) to enhance both visual and textual embeddings. Experimental results across three datasets demonstrate that Malenia outperforms state-of-the-art (SOTA) methods in zero-shot lesion segmentation.
weaknesses: 1. The method relies on transforming patient reports into structured descriptions of eight visual attributes, which requires expert radiologist involvement. This process may not be scalable or practical in real-world applications where such annotations are not readily available.
2. As noted in Appendix D, the model performs less effectively on attributes requiring complex visual semantic understanding, such as "Surface Characteristics" and "Specific Features". This suggests a potential limitation in capturing intricate visual patterns.
3. While the model shows strong performance on 12 lesion categories, the diversity of lesions in clinical practice is vast. The ability of Malenia to generalize to a wider range of unseen lesions without additional training remains uncertain.

Meta-review: This paper introduces Malenia, a framework for zero-shot lesion segmentation in 3D medical images, leveraging vision-language pre-training to align multi-scale mask representations with textual embeddings of lesion attributes. By decomposing radiological reports into attributes using LLMs and human annotators, the framework achieves fine-grained alignment between visual and textual features, enhancing its ability to generalize to unseen lesions. Through comprehensive experiments across three datasets and 12 lesion categories, Malenia demonstrates improved segmentation performance over existing zero-shot segmentation methods.

Strength: This approach to decomposing free-text reports into structured attributes reduces ambiguity and improves performance on unseen lesions. The framework addresses a critical need in clinical settings for handling diverse, emerging anomalies without labeled data. Empirical validation across multiple datasets highlights its robustness and effectiveness.

Weakness: Reviewers raise concerns about the method’s similarity with prior work, the privacy concerns by using GPT-4 for attribute extraction, lacking the extension to other imaging modalities and how to handle ambiguous lesion boundaries. The author has addressed these concerns well in the rebuttal period. 

Considering the paper’s contribution based on the promising performance as well as the improved revision during rebuttal, I would lean to accept.

Decision: Accept (Poster)