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Single Channel Breast Segmentation Deeplabv3plus
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Single Channel Breast Segmentation Deeplabv3plus

Developed by AnikiFan
PyTorch-based DeepLabV3+ image segmentation model supporting multiple encoder architectures
Image Segmentation
Safetensors
Open Source License:MIT#Medical Image Segmentation#High-Precision Semantic Segmentation#ResNet Encoder
Downloads 18
Release Time : 10/15/2024

Model Overview

DeepLabV3+ is an advanced semantic segmentation model combining dilated convolutions with encoder-decoder structure, suitable for high-precision pixel-level image segmentation tasks

Model Features

Flexible Encoder Selection
Supports multiple pretrained encoders (e.g. ResNet34) to accommodate different computational resource requirements
Atrous Spatial Pyramid Pooling
Uses ASPP module to capture multi-scale contextual information, improving segmentation accuracy
Encoder-Decoder Structure
Combines low-level features with high-level semantic information for precise boundary segmentation

Model Capabilities

Medical Image Segmentation
Semantic Segmentation
Pixel-Level Classification
Image Analysis

Use Cases

Medical Imaging
Breast Tissue Segmentation
Used for precise segmentation of tissues in breast medical images
Achieved 0.748 IoU metric on test set
Remote Sensing
Land Cover Classification
Segmentation of land cover types in satellite images
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