ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation

• URL: http://arxiv.org/abs/2312.07969v2
• Date: Mon, 20 May 2024 11:55:42 GMT
• Title: ASLseg: Adapting SAM in the Loop for Semi-supervised Liver Tumor Segmentation
• Authors: Shiyun Chen, Li Lin, Pujin Cheng, Xiaoying Tang,
• Abstract summary: Liver tumor segmentation is essential for computer-aided diagnosis, surgical planning, and prognosis evaluation. Semi-Supervised Learning (SSL) is a common technique to address these challenges. We propose a novel semi-supervised framework, named ASLseg, which can effectively adapt the SAM to the SSL setting.
• Score: 2.3617131367115705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Liver tumor segmentation is essential for computer-aided diagnosis, surgical planning, and prognosis evaluation. However, obtaining and maintaining a large-scale dataset with dense annotations is challenging. Semi-Supervised Learning (SSL) is a common technique to address these challenges. Recently, Segment Anything Model (SAM) has shown promising performance in some medical image segmentation tasks, but it performs poorly for liver tumor segmentation. In this paper, we propose a novel semi-supervised framework, named ASLseg, which can effectively adapt the SAM to the SSL setting and combine both domain-specific and general knowledge of liver tumors. Specifically, the segmentation model trained with a specific SSL paradigm provides the generated pseudo-labels as prompts to the fine-tuned SAM. An adaptation network is then used to refine the SAM-predictions and generate higher-quality pseudo-labels. Finally, the reliable pseudo-labels are selected to expand the labeled set for iterative training. Extensive experiments on the LiTS dataset demonstrate overwhelming performance of our ASLseg.

Related papers

• EP-SAM: Weakly Supervised Histopathology Segmentation via Enhanced Prompt with Segment Anything [3.760646312664378]
  Pathological diagnosis of diseases like cancer has conventionally relied on the evaluation of morphological features by physicians and pathologists. Recent advancements in compute-aided diagnosis (CAD) systems are gaining significant attention as diagnostic support tools. We present a weakly supervised semantic segmentation (WSSS) model by combining class activation map and Segment Anything Model (SAM)-based pseudo-labeling.
  arXiv  Detail & Related papers  (2024-10-17T14:55:09Z)
• SAM-Driven Weakly Supervised Nodule Segmentation with Uncertainty-Aware Cross Teaching [13.5553526185399]
  Automated nodule segmentation is essential for computer-assisted diagnosis in ultrasound images. Recently, segmentation foundation models like SAM have shown impressive generalizability on natural images. In this work, we devise a novel weakly supervised framework that effectively utilizes the segmentation foundation model to generate pseudo-labels.
  arXiv  Detail & Related papers  (2024-07-18T14:27:54Z)
• Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medical Image Segmentation [44.54301473673582]
  Semi-supervised learning (SSL) has achieved notable progress in medical image segmentation. Recent developments in visual foundation models, such as the Segment Anything Model (SAM), have demonstrated remarkable adaptability. We propose a cross-prompting consistency method with segment anything model (CPC-SAM) for semi-supervised medical image segmentation.
  arXiv  Detail & Related papers  (2024-07-07T15:43:20Z)
• ASPS: Augmented Segment Anything Model for Polyp Segmentation [77.25557224490075]
  The Segment Anything Model (SAM) has introduced unprecedented potential for polyp segmentation. SAM's Transformer-based structure prioritizes global and low-frequency information. CFA integrates a trainable CNN encoder branch with a frozen ViT encoder, enabling the integration of domain-specific knowledge.
  arXiv  Detail & Related papers  (2024-06-30T14:55:32Z)
• Generalized Semi-Supervised Learning via Self-Supervised Feature Adaptation [87.17768598044427]
  Traditional semi-supervised learning assumes that the feature distributions of labeled and unlabeled data are consistent. We propose Self-Supervised Feature Adaptation (SSFA), a generic framework for improving SSL performance when labeled and unlabeled data come from different distributions. Our proposed SSFA is applicable to various pseudo-label-based SSL learners and significantly improves performance in labeled, unlabeled, and even unseen distributions.
  arXiv  Detail & Related papers  (2024-05-31T03:13:45Z)
• PosSAM: Panoptic Open-vocabulary Segment Anything [58.72494640363136]
  PosSAM is an open-vocabulary panoptic segmentation model that unifies the strengths of the Segment Anything Model (SAM) with the vision-native CLIP model in an end-to-end framework. We introduce a Mask-Aware Selective Ensembling (MASE) algorithm that adaptively enhances the quality of generated masks and boosts the performance of open-vocabulary classification during inference for each image.
  arXiv  Detail & Related papers  (2024-03-14T17:55:03Z)
• Self-Supervised Multiple Instance Learning for Acute Myeloid Leukemia Classification [1.1874560263468232]
  Diseases like Acute Myeloid Leukemia (AML) pose challenges due to scarce and costly annotations on a single-cell level. Multiple Instance Learning (MIL) addresses weakly labeled scenarios but necessitates powerful encoders typically trained with labeled data. In this study, we explore Self-Supervised Learning (SSL) as a pre-training approach for MIL-based subtype AML classification from blood smears.
  arXiv  Detail & Related papers  (2024-03-08T15:16:15Z)
• Self-supervised TransUNet for Ultrasound regional segmentation of the distal radius in children [0.6291443816903801]
  Masked Autoencoder for SSL (SSL-MAE) of TransUNet, for segmenting bony regions from children's wrist ultrasound scans. This paper investigates the feasibility of deploying the Masked Autoencoder for SSL (SSL-MAE) of TransUNet, for segmenting bony regions from children's wrist ultrasound scans.
  arXiv  Detail & Related papers  (2023-09-18T05:23:33Z)
• SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation [65.52097667738884]
  We introduce SurgicalSAM, a novel end-to-end efficient-tuning approach for SAM to integrate surgical-specific information with SAM's pre-trained knowledge for improved generalisation. Specifically, we propose a lightweight prototype-based class prompt encoder for tuning, which directly generates prompt embeddings from class prototypes. In addition, to address the low inter-class variance among surgical instrument categories, we propose contrastive prototype learning.
  arXiv  Detail & Related papers  (2023-08-17T02:51:01Z)
• Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images [83.7047542725469]
  Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development. We present a method, named Label Cleaning Multiple Instance Learning (LC-MIL), to refine coarse annotations on a single WSI without the need of external training data. Our experiments on a heterogeneous WSI set with breast cancer lymph node metastasis, liver cancer, and colorectal cancer samples show that LC-MIL significantly refines the coarse annotations, outperforming the state-of-the-art alternatives, even while learning from a single slide.
  arXiv  Detail & Related papers  (2021-09-22T15:06:06Z)
• Trash to Treasure: Harvesting OOD Data with Cross-Modal Matching for Open-Set Semi-Supervised Learning [101.28281124670647]
  Open-set semi-supervised learning (open-set SSL) investigates a challenging but practical scenario where out-of-distribution (OOD) samples are contained in the unlabeled data. We propose a novel training mechanism that could effectively exploit the presence of OOD data for enhanced feature learning. Our approach substantially lifts the performance on open-set SSL and outperforms the state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2021-08-12T09:14:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.