# ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanism

arXiv: https://arxiv.org/abs/2411.04656 · HTML: https://mlsanity.com/paper/2411.04656/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 12 of 12)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Segment Anything in Medical Images](https://mlsanity.com/paper/2304.12306/) (2023)
- [Self Pre-training with Masked Autoencoders for Medical Image Classification and Segmentation](https://mlsanity.com/paper/2203.05573/) (2022)
- [EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation](https://mlsanity.com/paper/2307.08473/) (2023)
- [Constrained unsupervised anomaly segmentation](https://mlsanity.com/paper/2203.01671/) (2022)
- [ICHPro: Intracerebral Hemorrhage Prognosis Classification Via Joint-attention Fusion-based 3d Cross-modal Network](https://mlsanity.com/paper/2402.11307/) (2024)
- [Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation](https://mlsanity.com/paper/2303.10049/) (2023)
- [Treatment Outcome Prediction for Intracerebral Hemorrhage via Generative Prognostic Model with Imaging and Tabular Data](https://mlsanity.com/paper/2307.12858/) (2023)
- [GCS-ICHNet: Assessment of Intracerebral Hemorrhage Prognosis using Self-Attention with Domain Knowledge Integration](https://mlsanity.com/paper/2311.04772/) (2023)

## Built on by (most cited first, 16 of 16)

- [Set You Straight: Auto-Steering Denoising Trajectories to Sidestep Unwanted Concepts](https://mlsanity.com/paper/2504.12782/) (2025)
- [TAB: Unified Benchmarking of Time Series Anomaly Detection Methods](https://mlsanity.com/paper/2506.18046/) (2025)
- [CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentation](https://mlsanity.com/paper/2506.23121/) (2025)
- [Hy-Facial: Hybrid Feature Extraction by Dimensionality Reduction Methods for Enhanced Facial Expression Classification](https://mlsanity.com/paper/2509.26614/) (2025)
- [K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2505.23017/) (2025)
- [Overview of the NLPCC 2025 Shared Task 4: Multi-modal, Multilingual, and Multi-hop Medical Instructional Video Question Answering Challenge](https://mlsanity.com/paper/2505.06814/) (2025)
- [A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planning](https://mlsanity.com/paper/2506.07236/) (2025)
- [Cognitive-Inspired Hierarchical Attention Fusion With Visual and Textual for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2504.15085/) (2025)
- [TRACE: Trajectory-Constrained Concept Erasure in Diffusion Models](https://mlsanity.com/paper/2505.23312/) (2025)
- [Hierarchical Modeling for Medical Visual Question Answering with Cross-Attention Fusion](https://mlsanity.com/paper/2504.03135/) (2025)
- [Dual form Complementary Masking for Domain-Adaptive Image Segmentation](https://mlsanity.com/paper/2507.12008/) (2025)
- [Small Lesions-aware Bidirectional Multimodal Multiscale Fusion Network for Lung Disease Classification](https://mlsanity.com/paper/2508.04205/) (2025)
- [FADE: Adversarial Concept Erasure in Flow Models](https://mlsanity.com/paper/2507.12283/) (2025)
- [RefLSM: Linearized Structural-Prior Reflectance Model for Medical Image Segmentation and Bias-Field Correction](https://mlsanity.com/paper/2512.07191/) (2025)
- [USCNet: Transformer-Based Multimodal Fusion with Segmentation Guidance for Urolithiasis Classification](https://mlsanity.com/paper/2604.07141/) (2026)
- [CLIP-Guided SAM: Parameter-Efficient Semantic Conditioning for Promptable Segmentation](https://mlsanity.com/paper/2605.24807/) (2026)

## Abstract (arXiv)

Intracerebral hemorrhage (ICH) is the most fatal subtype of stroke and is characterized by a high incidence of disability. Accurate segmentation of the ICH region and prognosis prediction are critically important for developing and refining treatment plans for post-ICH patients. However, existing approaches address these two tasks independently and predominantly focus on imaging data alone, thereby neglecting the intrinsic correlation between the tasks and modalities. This paper introduces a multi-task network, ICH-SCNet, designed for both ICH segmentation and prognosis classification. Specifically, we integrate a SAM-CLIP cross-modal interaction mechanism that combines medical text and segmentation auxiliary information with neuroimaging data to enhance cross-modal feature recognition. Additionally, we develop an effective feature fusion module and a multi-task loss function to improve performance further. Extensive experiments on an ICH dataset reveal that our approach surpasses other state-of-the-art methods. It excels in the overall performance of classification tasks and outperforms competing models in all segmentation task metrics.