# Semantic Segmentation and Object Detection Towards Instance Segmentation: Breast Tumor Identification

Source: [Semantic Segmentation and Object Detection Towards Instance Segmentation: Breast Tumor Identification](<https://www.sota2.com/research/paper/68fa6a7a1e209131cea28680>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:26:28.956Z

Authors: Mohamed Mejri, Aymen Mejri, Oumayma Mejri, Chiraz Fekih

Published: 2021-08-06T20:02:46.000Z

## Abstract

Breast cancer is one of the factors that cause the increase of mortality of women. The most widely used method for diagnosing this geological disease i.e. breast cancer is the ultrasound scan. Several key features such as the smoothness and the texture of the tumor captured through ultrasound scans encode the abnormality of the breast tumors (malignant from benign). However, ultrasound scans are often noisy and include irrelevant parts of the breast that may bias the segmentation of eventual tumors. In this paper, we are going to extract the region of interest ( i.e, bounding boxes of the tumors) and feed-forward them to one semantic segmentation encoder-decoder structure based on its classification (i.e, malignant or benign). the whole process aims to build an instance-based segmenter from a semantic segmenter and an object detector.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2108.03287>)

[PDF](<https://arxiv.org/pdf/2108.03287.pdf>)