# Multi-scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Images

arXiv: https://arxiv.org/abs/2105.06238 · HTML: https://mlsanity.com/paper/2105.06238/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 13 May 2021.

## Builds on (most cited first, 7 of 7)

- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions](https://mlsanity.com/paper/1909.00166/) (2019)
- [SCNet: Training Inference Sample Consistency for Instance Segmentation](https://mlsanity.com/paper/2012.10150/) (2020)
- [Semi-supervised few-shot learning for medical image segmentation](https://mlsanity.com/paper/2003.08462/) (2020)
- [Mask-RCNN and U-net Ensembled for Nuclei Segmentation](https://mlsanity.com/paper/1901.10170/) (2019)
- [Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation](https://mlsanity.com/paper/2003.05056/) (2020)
- [Novel and Tuneable Method for Skin Detection Based on Hybrid Color Space and Color Statistical Features](https://mlsanity.com/paper/1407.6506/) (2014)

## Built on by (most cited first, 11 of 11)

- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2207.08518/) (2022)
- [TransNorm: Transformer Provides a Strong Spatial Normalization Mechanism for a Deep Segmentation Model](https://mlsanity.com/paper/2207.13415/) (2022)
- [Contextual Attention Network: Transformer Meets U-Net](https://mlsanity.com/paper/2203.01932/) (2022)
- [SMU-Net: Style matching U-Net for brain tumor segmentation with missing modalities](https://mlsanity.com/paper/2204.02961/) (2022)
- [DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2212.13504/) (2022)
- [Medical Image Segmentation on MRI Images with Missing Modalities: A Review](https://mlsanity.com/paper/2203.06217/) (2022)
- [Enhancing Medical Image Segmentation with TransCeption: A Multi-Scale Feature Fusion Approach](https://mlsanity.com/paper/2301.10847/) (2023)
- [MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model](https://mlsanity.com/paper/2410.05905/) (2024)
- [Intervertebral Disc Labeling With Learning Shape Information, A Look Once Approach](https://mlsanity.com/paper/2204.02943/) (2022)
- [Segmentation of Multiple Myeloma Plasma Cells in Microscopy Images with Noisy Labels](https://mlsanity.com/paper/2111.05125/) (2021)

## Abstract (arXiv)

Multiple myeloma cancer is a type of blood cancer that happens when the growth of abnormal plasma cells becomes out of control in the bone marrow. There are various ways to diagnose multiple myeloma in bone marrow such as complete blood count test (CBC) or counting myeloma plasma cell in aspirate slide images using manual visualization or through image processing technique. In this work, an automatic deep learning method for the detection and segmentation of multiple myeloma plasma cell have been explored. To this end, a two-stage deep learning method is designed. In the first stage, the nucleus detection network is utilized to extract each instance of a cell of interest. The extracted instance is then fed to the multi-scale function to generate a multi-scale representation. The objective of the multi-scale function is to capture the shape variation and reduce the effect of object scale on the cytoplasm segmentation network. The generated scales are then fed into a pyramid of cytoplasm networks to learn the segmentation map in various scales. On top of the cytoplasm segmentation network, we included a scale aggregation function to refine and generate a final prediction. The proposed approach has been evaluated on the SegPC2021 grand-challenge and ranked second on the final test phase among all teams.