# Multi-task Explainable Skin Lesion Classification

arXiv: https://arxiv.org/abs/2310.07209 · HTML: https://mlsanity.com/paper/2310.07209/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 67 references that are in the map.
- First posted on arXiv 11 Oct 2023.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models](https://mlsanity.com/paper/1708.08296/) (2017)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation](https://mlsanity.com/paper/1801.00926/) (2018)
- [Difficulty-aware Meta-learning for Rare Disease Diagnosis](https://mlsanity.com/paper/1907.00354/) (2019)
- [DSNet: Automatic Dermoscopic Skin Lesion Segmentation](https://mlsanity.com/paper/1907.04305/) (2019)
- [Meta-learning with implicit gradients in a few-shot setting for medical image segmentation](https://mlsanity.com/paper/2106.03223/) (2021)
- [Skin Lesion Classification Using CNNs with Patch-Based Attention and Diagnosis-Guided Loss Weighting](https://mlsanity.com/paper/1905.02793/) (2019)
- [Boosting Few-shot Semantic Segmentation with Transformers](https://mlsanity.com/paper/2108.02266/) (2021)
- [SkinNet: A Deep Learning Framework for Skin Lesion Segmentation](https://mlsanity.com/paper/1806.09522/) (2018)
- [PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning](https://mlsanity.com/paper/2108.11694/) (2021)
- [MTCD: Cataract Detection via Near Infrared Eye Images](https://mlsanity.com/paper/2110.02564/) (2021)
- [Dynamic Sub-Cluster-Aware Network for Few-Shot Skin Disease Classification](https://mlsanity.com/paper/2207.01072/) (2022)
- [Influential Prototypical Networks for Few Shot Learning: A Dermatological Case Study](https://mlsanity.com/paper/2111.00698/) (2021)

## Built on by (most cited first, 1 of 1)

- [Optimizing Skin Lesion Classification via Multimodal Data and Auxiliary Task Integration](https://mlsanity.com/paper/2402.10454/) (2024)

## Abstract (arXiv)

Skin cancer is one of the deadliest diseases and has a high mortality rate if left untreated. The diagnosis generally starts with visual screening and is followed by a biopsy or histopathological examination. Early detection can aid in lowering mortality rates. Visual screening can be limited by the experience of the doctor. Due to the long tail distribution of dermatological datasets and significant intra-variability between classes, automatic classification utilizing computer-aided methods becomes challenging. In this work, we propose a multitask few-shot-based approach for skin lesions that generalizes well with few labelled data to address the small sample space challenge. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network. The output of the segmentation network helps to focus on the most discriminatory features while making a decision by the classification network. To further enhance the classification performance, we have combined segmentation and classification loss in a weighted manner. We have also included the visualization results that explain the decisions made by the algorithm. Three dermatological datasets are used to evaluate the proposed method thoroughly. We also conducted cross-database experiments to ensure that the proposed approach is generalizable across similar datasets. Experimental results demonstrate the efficacy of the proposed work.